ch12 scipy.stats chi2_contingency(pd.crosstab()).ipynb

{
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  {
   "cell_type": "markdown",
   "id": "7bfddb6d",
   "metadata": {},
   "source": [
    "# 12차시 범주형 변수 간의 독립성 검정(Chi-squared test)\n",
    "\n",
    "## 01 독립성 검정 개요\n",
    "\n",
    "### 독립성 검정의 특징\n",
    "\n",
    "- 두 명목형 변수를 대상으로 실시하는 분석\n",
    "- 독립 관점에서의 해석과 연관 관점에서의 해석이 존재\n",
    "- 연속형 변수의 경우 명목형 변수로 변환 후 실시\n",
    "\n",
    "### 가설\n",
    "\n",
    "- 귀무가설(H0): 두 변수는 서로 독립임 (연관X)\n",
    "- 대립가설(H1): 두 변수는 서로 독립이 아님 (연관O)\n",
    "\n",
    "## 02 주요 함수 및 메서드 소개\n",
    "\n",
    "### scipy - chi2_contingency\n",
    "\n",
    "- scipy의 독립성 검정을 실시하는 함수\n",
    "- 입력은 두 개의 명목형 변수의 각 원소의 빈도\n",
    "- 출력은 검정통계량, p-value, 자유도, 기대도수 4개의 연산 결과가 튜플로 산출"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b37153ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from scipy.stats import chi2_contingency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "601737c2",
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>is_attrited</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Dependent_cnt</th>\n",
       "      <th>Edu_level</th>\n",
       "      <th>Marital_status</th>\n",
       "      <th>Income</th>\n",
       "      <th>Card</th>\n",
       "      <th>Period_m</th>\n",
       "      <th>Total_rel_cnt</th>\n",
       "      <th>Inactive_last_12m</th>\n",
       "      <th>Contacts_cnt_last_12m</th>\n",
       "      <th>Credit_limit</th>\n",
       "      <th>Total_trans_amt</th>\n",
       "      <th>Total_trans_cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>9996</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Married</td>\n",
       "      <td>$40K - $60K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7758.0</td>\n",
       "      <td>569</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>9997</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>M</td>\n",
       "      <td>4</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Married</td>\n",
       "      <td>$60K - $80K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6905.0</td>\n",
       "      <td>1370</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>9998</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>Uneducated</td>\n",
       "      <td>Single</td>\n",
       "      <td>$60K - $80K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>36</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5489.0</td>\n",
       "      <td>3215</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>9999</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>$40K - $60K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4878.0</td>\n",
       "      <td>5021</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Single</td>\n",
       "      <td>Less than $40K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>36</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1438.3</td>\n",
       "      <td>4463</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID  is_attrited  Age Gender  Dependent_cnt   Edu_level  \\\n",
       "9995   9996            1   36      M              2    Graduate   \n",
       "9996   9997            0   54      M              4    Graduate   \n",
       "9997   9998            0   46      M              3  Uneducated   \n",
       "9998   9999            0   43      M              3    Graduate   \n",
       "9999  10000            0   45      F              3     Unknown   \n",
       "\n",
       "     Marital_status          Income  Card  Period_m  Total_rel_cnt  \\\n",
       "9995        Married     $40K - $60K  Blue        18              3   \n",
       "9996        Married     $60K - $80K  Blue        36              4   \n",
       "9997         Single     $60K - $80K  Blue        36              5   \n",
       "9998        Unknown     $40K - $60K  Blue        36              2   \n",
       "9999         Single  Less than $40K  Blue        36              3   \n",
       "\n",
       "      Inactive_last_12m  Contacts_cnt_last_12m  Credit_limit  Total_trans_amt  \\\n",
       "9995                  1                      3        7758.0              569   \n",
       "9996                  3                      3        6905.0             1370   \n",
       "9997                  1                      2        5489.0             3215   \n",
       "9998                  3                      3        4878.0             5021   \n",
       "9999                  4                      0        1438.3             4463   \n",
       "\n",
       "      Total_trans_cnt  \n",
       "9995               23  \n",
       "9996               25  \n",
       "9997               64  \n",
       "9998               84  \n",
       "9999               71  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"강의자료/실습파일/financial_info_10k_persons.csv\")\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "97e2ed72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4.093468963560284,\n",
       " 0.2515464475739655,\n",
       " 3,\n",
       " array([[ 392.8524, 2462.9028, 2067.3924,  392.8524],\n",
       "        [ 346.1476, 2170.0972, 1821.6076,  346.1476]]))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chi2_contingency(pd.crosstab(df[\"Gender\"], df[\"Marital_status\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "faadfe88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.093468963560284\n",
      "0.2515464475739655\n"
     ]
    }
   ],
   "source": [
    "stat, p, dof, e_val = chi2_contingency(pd.crosstab(df[\"Gender\"],\n",
    "                                                   df[\"Marital_status\"]))\n",
    "print(stat)\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35953093",
   "metadata": {},
   "source": [
    "## Q1 고객의 학력이 고졸인 경우 성별과 이탈여부를 사용하여 독립성 검정을 실시했을 때 p-value는?\n",
    "1) 연속성 수정을 적용하지 않음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a1ede70d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>Dependent_cnt</th>\n",
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       "      <th>Contacts_cnt_last_12m</th>\n",
       "      <th>Credit_limit</th>\n",
       "      <th>Total_trans_amt</th>\n",
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       "      <td>6905.0</td>\n",
       "      <td>1370</td>\n",
       "      <td>25</td>\n",
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       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>9998</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>Uneducated</td>\n",
       "      <td>Single</td>\n",
       "      <td>$60K - $80K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>36</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5489.0</td>\n",
       "      <td>3215</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>9999</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>$40K - $60K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4878.0</td>\n",
       "      <td>5021</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Single</td>\n",
       "      <td>Less than $40K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>36</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1438.3</td>\n",
       "      <td>4463</td>\n",
       "      <td>71</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "         ID  is_attrited  Age Gender  Dependent_cnt   Edu_level  \\\n",
       "9995   9996            1   36      M              2    Graduate   \n",
       "9996   9997            0   54      M              4    Graduate   \n",
       "9997   9998            0   46      M              3  Uneducated   \n",
       "9998   9999            0   43      M              3    Graduate   \n",
       "9999  10000            0   45      F              3     Unknown   \n",
       "\n",
       "     Marital_status          Income  Card  Period_m  Total_rel_cnt  \\\n",
       "9995        Married     $40K - $60K  Blue        18              3   \n",
       "9996        Married     $60K - $80K  Blue        36              4   \n",
       "9997         Single     $60K - $80K  Blue        36              5   \n",
       "9998        Unknown     $40K - $60K  Blue        36              2   \n",
       "9999         Single  Less than $40K  Blue        36              3   \n",
       "\n",
       "      Inactive_last_12m  Contacts_cnt_last_12m  Credit_limit  Total_trans_amt  \\\n",
       "9995                  1                      3        7758.0              569   \n",
       "9996                  3                      3        6905.0             1370   \n",
       "9997                  1                      2        5489.0             3215   \n",
       "9998                  3                      3        4878.0             5021   \n",
       "9999                  4                      0        1438.3             4463   \n",
       "\n",
       "      Total_trans_cnt  \n",
       "9995               23  \n",
       "9996               25  \n",
       "9997               64  \n",
       "9998               84  \n",
       "9999               71  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q1 = pd.read_csv(\"강의자료/실습파일/financial_info_10k_persons.csv\")\n",
    "Q1.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2d7153d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['High School', 'Uneducated', 'Doctorate', 'Unknown', 'Graduate',\n",
       "       'Post-Graduate', 'College'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q1[\"Edu_level\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "12795f44",
   "metadata": {
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       "      <th>9972</th>\n",
       "      <td>9973</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>F</td>\n",
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       "      <td>High School</td>\n",
       "      <td>Single</td>\n",
       "      <td>Unknown</td>\n",
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       "      <td>Less than $40K</td>\n",
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       "      <td>44</td>\n",
       "      <td>6</td>\n",
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       "      <td>3</td>\n",
       "      <td>2971.0</td>\n",
       "      <td>4458</td>\n",
       "      <td>63</td>\n",
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       "      <th>9982</th>\n",
       "      <td>9983</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>High School</td>\n",
       "      <td>Single</td>\n",
       "      <td>$40K - $60K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1561.0</td>\n",
       "      <td>4391</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9987</th>\n",
       "      <td>9988</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>High School</td>\n",
       "      <td>Married</td>\n",
       "      <td>Less than $40K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2590.0</td>\n",
       "      <td>3391</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9994</th>\n",
       "      <td>9995</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>High School</td>\n",
       "      <td>Married</td>\n",
       "      <td>$40K - $60K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5559.0</td>\n",
       "      <td>5344</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID  is_attrited  Age Gender  Dependent_cnt    Edu_level  \\\n",
       "9972  9973            0   36      F              3  High School   \n",
       "9981  9982            0   52      F              2  High School   \n",
       "9982  9983            0   41      F              2  High School   \n",
       "9987  9988            0   43      F              3  High School   \n",
       "9994  9995            0   38      F              1  High School   \n",
       "\n",
       "     Marital_status          Income  Card  Period_m  Total_rel_cnt  \\\n",
       "9972         Single         Unknown  Blue        23              6   \n",
       "9981         Single  Less than $40K  Blue        44              6   \n",
       "9982         Single     $40K - $60K  Blue        36              4   \n",
       "9987        Married  Less than $40K  Blue        28              3   \n",
       "9994        Married     $40K - $60K  Blue        19              1   \n",
       "\n",
       "      Inactive_last_12m  Contacts_cnt_last_12m  Credit_limit  Total_trans_amt  \\\n",
       "9972                  3                      4        2804.0             2812   \n",
       "9981                  3                      3        2971.0             4458   \n",
       "9982                  5                      3        1561.0             4391   \n",
       "9987                  3                      2        2590.0             3391   \n",
       "9994                  1                      2        5559.0             5344   \n",
       "\n",
       "      Total_trans_cnt  \n",
       "9972               77  \n",
       "9981               63  \n",
       "9982               67  \n",
       "9987               66  \n",
       "9994               82  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q1_HS = Q1[Q1[\"Edu_level\"] == \"High School\"]\n",
    "Q1_HS.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "67b5b6da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04605478481460392"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chi2_contingency(pd.crosstab(Q1_HS[\"Gender\"],\n",
    "                             Q1_HS[\"is_attrited\"]),\n",
    "                correction = False)[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b646306f",
   "metadata": {},
   "source": [
    "## Q2 성별에 따른 카드 등급은 서로 관련이 있는지 독립성 검정을 실시하고 해당 검정 결과의 검정 통계량은 얼마인가?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "066fbe7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(66.45702170623164,\n",
       " 2.4470625495771945e-14,\n",
       " 3,\n",
       " array([[4962.486 ,   59.0076,   10.632 ,  283.8744],\n",
       "        [4372.514 ,   51.9924,    9.368 ,  250.1256]]))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q2 = pd.read_csv(\"강의자료/실습파일/financial_info_10k_persons.csv\")\n",
    "chi2_contingency(pd.crosstab(Q2[\"Gender\"],\n",
    "                             Q2[\"Card\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09f235b6",
   "metadata": {},
   "source": [
    "## Q3 최근 12개월의 이용 실적 중 3개월 이상 사용 실적이 없는 것과 고객 이탈의 관계가 있는지 독립성 검정을 실시한 결과로 옳은 것은?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ab06d6b6",
   "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>ID</th>\n",
       "      <th>is_attrited</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Dependent_cnt</th>\n",
       "      <th>Edu_level</th>\n",
       "      <th>Marital_status</th>\n",
       "      <th>Income</th>\n",
       "      <th>Card</th>\n",
       "      <th>Period_m</th>\n",
       "      <th>Total_rel_cnt</th>\n",
       "      <th>Inactive_last_12m</th>\n",
       "      <th>Contacts_cnt_last_12m</th>\n",
       "      <th>Credit_limit</th>\n",
       "      <th>Total_trans_amt</th>\n",
       "      <th>Total_trans_cnt</th>\n",
       "      <th>is_Inactive_last_12m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>9996</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>M</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Married</td>\n",
       "      <td>$40K - $60K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7758.0</td>\n",
       "      <td>569</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>9997</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>M</td>\n",
       "      <td>4</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Married</td>\n",
       "      <td>$60K - $80K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>36</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6905.0</td>\n",
       "      <td>1370</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>9998</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>Uneducated</td>\n",
       "      <td>Single</td>\n",
       "      <td>$60K - $80K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>36</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5489.0</td>\n",
       "      <td>3215</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>9999</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>$40K - $60K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>36</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4878.0</td>\n",
       "      <td>5021</td>\n",
       "      <td>84</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>10000</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>F</td>\n",
       "      <td>3</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Single</td>\n",
       "      <td>Less than $40K</td>\n",
       "      <td>Blue</td>\n",
       "      <td>36</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1438.3</td>\n",
       "      <td>4463</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID  is_attrited  Age Gender  Dependent_cnt   Edu_level  \\\n",
       "9995   9996            1   36      M              2    Graduate   \n",
       "9996   9997            0   54      M              4    Graduate   \n",
       "9997   9998            0   46      M              3  Uneducated   \n",
       "9998   9999            0   43      M              3    Graduate   \n",
       "9999  10000            0   45      F              3     Unknown   \n",
       "\n",
       "     Marital_status          Income  Card  Period_m  Total_rel_cnt  \\\n",
       "9995        Married     $40K - $60K  Blue        18              3   \n",
       "9996        Married     $60K - $80K  Blue        36              4   \n",
       "9997         Single     $60K - $80K  Blue        36              5   \n",
       "9998        Unknown     $40K - $60K  Blue        36              2   \n",
       "9999         Single  Less than $40K  Blue        36              3   \n",
       "\n",
       "      Inactive_last_12m  Contacts_cnt_last_12m  Credit_limit  Total_trans_amt  \\\n",
       "9995                  1                      3        7758.0              569   \n",
       "9996                  3                      3        6905.0             1370   \n",
       "9997                  1                      2        5489.0             3215   \n",
       "9998                  3                      3        4878.0             5021   \n",
       "9999                  4                      0        1438.3             4463   \n",
       "\n",
       "      Total_trans_cnt  is_Inactive_last_12m  \n",
       "9995               23                     0  \n",
       "9996               25                     1  \n",
       "9997               64                     0  \n",
       "9998               84                     1  \n",
       "9999               71                     1  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q3 = pd.read_csv(\"강의자료/실습파일/financial_info_10k_persons.csv\")\n",
    "Q3[\"is_Inactive_last_12m\"] = (Q3[\"Inactive_last_12m\"] >= 3) + 0\n",
    "Q3.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e056f886",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(210.74834292621756,\n",
       " 9.431916381955241e-48,\n",
       " 1,\n",
       " array([[4606.4112,  869.5888],\n",
       "        [3805.5888,  718.4112]]))"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chi2_contingency(pd.crosstab(Q3[\"is_Inactive_last_12m\"],\n",
    "                            Q3[\"is_attrited\"]))"
   ]
  }
 ],
 "metadata": {
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