4장_판다스_데이터_분석_(4.4).ipynb

{
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  {
   "cell_type": "markdown",
   "id": "180bd3b0",
   "metadata": {},
   "source": [
    "# 데이터프레임의 데이터 조작"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c09e2927",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.0\n",
       "1    1.0\n",
       "2    2.0\n",
       "3    NaN\n",
       "4    4.0\n",
       "5    5.0\n",
       "6    6.0\n",
       "7    7.0\n",
       "8    8.0\n",
       "9    9.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "s = pd.Series(range(10))\n",
    "s[3] = np.nan\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6e4b1f1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fd59106d",
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
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       "      <th>3</th>\n",
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       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>3</th>\n",
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      "text/plain": [
       "     0    1    2    3\n",
       "0  3.0  4.0  2.0  4.0\n",
       "1  4.0  1.0  2.0  2.0\n",
       "2  2.0  4.0  3.0  NaN\n",
       "3  4.0  1.0  3.0  1.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "df = pd.DataFrame(np.random.randint(5, size=(4, 4)), dtype=float)\n",
    "df.iloc[2, 3] = np.nan\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8017dc35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4\n",
       "1    4\n",
       "2    4\n",
       "3    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()    # 누락 부분 찾기 용이 (열별)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2ec76229",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "titanic = sns.load_dataset(\"titanic\")\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fd95a3a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived       891\n",
       "pclass         891\n",
       "sex            891\n",
       "age            714\n",
       "sibsp          891\n",
       "parch          891\n",
       "fare           891\n",
       "embarked       889\n",
       "class          891\n",
       "who            891\n",
       "adult_male     891\n",
       "deck           203\n",
       "embark_town    889\n",
       "alive          891\n",
       "alone          891\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "33e4c7f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95    4\n",
       "96    5\n",
       "97    2\n",
       "98    4\n",
       "99    3\n",
       "dtype: int32"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(1)\n",
    "s2 = pd.Series(np.random.randint(6, size=100))\n",
    "s2.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "90970bd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    22\n",
       "0    18\n",
       "4    17\n",
       "5    16\n",
       "3    14\n",
       "2    13\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2d6bdcbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    18\n",
       "1    22\n",
       "2    13\n",
       "3    14\n",
       "4    17\n",
       "5    16\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2.value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4241acbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    13\n",
       "3    14\n",
       "5    16\n",
       "4    17\n",
       "0    18\n",
       "1    22\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2.value_counts().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "df4e23b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.0\n",
       "1    1.0\n",
       "2    2.0\n",
       "4    4.0\n",
       "5    5.0\n",
       "6    6.0\n",
       "7    7.0\n",
       "8    8.0\n",
       "9    9.0\n",
       "3    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7538b147",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9    9.0\n",
       "8    8.0\n",
       "7    7.0\n",
       "6    6.0\n",
       "5    5.0\n",
       "4    4.0\n",
       "2    2.0\n",
       "1    1.0\n",
       "0    0.0\n",
       "3    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b60cfcf1",
   "metadata": {},
   "source": [
    "데이터프레임에서 sort_values 메서드를 사용하려면 by 인수로 정렬 기준이 되는 열을 지정해 주어야 한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "62ce3225",
   "metadata": {},
   "outputs": [
    {
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       "     0    1    2    3\n",
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       "2  2.0  4.0  3.0  NaN\n",
       "3  4.0  1.0  3.0  1.0"
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    "df"
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   "execution_count": 23,
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   "outputs": [
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       "      <td>3.0</td>\n",
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       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "     0    1    2    3\n",
       "1  4.0  1.0  2.0  2.0\n",
       "3  4.0  1.0  3.0  1.0\n",
       "0  3.0  4.0  2.0  4.0\n",
       "2  2.0  4.0  3.0  NaN"
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     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3e9474fe",
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7f44110b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "male      577\n",
       "female    314\n",
       "dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.sort_values(\"sex\").value_counts(\"sex\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "54517a47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age\n",
       "24.00    30\n",
       "22.00    27\n",
       "18.00    26\n",
       "30.00    25\n",
       "28.00    25\n",
       "         ..\n",
       "20.50     1\n",
       "14.50     1\n",
       "12.00     1\n",
       "0.92      1\n",
       "80.00     1\n",
       "Length: 88, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.sort_values(\"age\").value_counts(\"age\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bf37c5a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class\n",
       "Third     491\n",
       "First     216\n",
       "Second    184\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "titanic.sort_values(\"class\").value_counts(\"class\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a4f9ecff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "alive\n",
       "no     549\n",
       "yes    342\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.sort_values(\"alive\").value_counts(\"alive\")"
   ]
  },
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   "execution_count": 39,
   "id": "c78c5951",
   "metadata": {},
   "outputs": [
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    "np.random.seed(1)\n",
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   "id": "7e707fc6",
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       "colTotal  24.0  33.0  25.0  24.0  15.0  10.0  5.0  16.0   152.0"
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     "execution_count": 44,
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    "df2"
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   "execution_count": 87,
   "id": "f1f888ac",
   "metadata": {},
   "outputs": [
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       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>B</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "0           0       3    male  22.0      1      0   7.2500        S   Third   \n",
       "1           1       1  female  38.0      1      0  71.2833        C   First   \n",
       "2           1       3  female  26.0      0      0   7.9250        S   Third   \n",
       "3           1       1  female  35.0      1      0  53.1000        S   First   \n",
       "4           0       3    male  35.0      0      0   8.0500        S   Third   \n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
       "886         0       2    male  27.0      0      0  13.0000        S  Second   \n",
       "887         1       1  female  19.0      0      0  30.0000        S   First   \n",
       "888         0       3  female   NaN      1      2  23.4500        S   Third   \n",
       "889         1       1    male  26.0      0      0  30.0000        C   First   \n",
       "890         0       3    male  32.0      0      0   7.7500        Q   Third   \n",
       "\n",
       "       who  adult_male deck  embark_town alive  alone  \n",
       "0      man        True  NaN  Southampton    no  False  \n",
       "1    woman       False    C    Cherbourg   yes  False  \n",
       "2    woman       False  NaN  Southampton   yes   True  \n",
       "3    woman       False    C  Southampton   yes  False  \n",
       "4      man        True  NaN  Southampton    no   True  \n",
       "..     ...         ...  ...          ...   ...    ...  \n",
       "886    man        True  NaN  Southampton    no   True  \n",
       "887  woman       False    B  Southampton   yes   True  \n",
       "888  woman       False  NaN  Southampton    no  False  \n",
       "889    man        True    C    Cherbourg   yes   True  \n",
       "890    man        True  NaN   Queenstown    no   True  \n",
       "\n",
       "[891 rows x 15 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "2410e53b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.69911764705882"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[\"age\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "b4d629a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27.915708812260537"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[titanic[\"sex\"] == \"female\"][\"age\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "9ff3b59f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34.61176470588235"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[(titanic[\"sex\"] == \"female\") & (titanic[\"class\"] == \"First\")][\"age\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "d698dafd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   A  B  C\n",
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       "4  4  3  4"
      ]
     },
     "execution_count": 98,
     "metadata": {},
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   "source": [
    "df3 = pd.DataFrame({\n",
    "    'A': [1, 3, 4, 3, 4],\n",
    "    'B': [2, 3, 1, 2, 3],\n",
    "    'C': [1, 5, 2, 4, 4]\n",
    "})\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "8bd1ade2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "A    3\n",
       "B    2\n",
       "C    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df3.apply(lambda x: x.max() - x.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "38eaa300",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "0    1\n",
       "1    2\n",
       "2    3\n",
       "3    2\n",
       "4    1\n",
       "dtype: int64"
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     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df3.apply(lambda x: x.max() - x.min(), axis = 1)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "00d2414d",
   "metadata": {},
   "outputs": [
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       "     A    B    C\n",
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       "4  2.0  NaN  2.0\n",
       "5  NaN  NaN  1.0"
      ]
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     "execution_count": 103,
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   "source": [
    "df3.apply(pd.value_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "bbaf3d47",
   "metadata": {},
   "outputs": [
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       "      <td>7.75</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch   fare embarked   class  \\\n",
       "886         0       2    male  27.0      0      0  13.00        S  Second   \n",
       "887         1       1  female  19.0      0      0  30.00        S   First   \n",
       "888         0       3  female   NaN      1      2  23.45        S   Third   \n",
       "889         1       1    male  26.0      0      0  30.00        C   First   \n",
       "890         0       3    male  32.0      0      0   7.75        Q   Third   \n",
       "\n",
       "       who  adult_male deck  embark_town alive  alone  \n",
       "886    man        True  NaN  Southampton    no   True  \n",
       "887  woman       False    B  Southampton   yes   True  \n",
       "888  woman       False  NaN  Southampton    no  False  \n",
       "889    man        True    C    Cherbourg   yes   True  \n",
       "890    man        True  NaN   Queenstown    no   True  "
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "213c839f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
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       "      <td>0</td>\n",
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       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch   fare embarked   class  \\\n",
       "886         0       2    male  27.0      0      0  13.00        S  Second   \n",
       "887         1       1  female  19.0      0      0  30.00        S   First   \n",
       "888         0       3  female   NaN      1      2  23.45        S   Third   \n",
       "889         1       1    male  26.0      0      0  30.00        C   First   \n",
       "890         0       3    male  32.0      0      0   7.75        Q   Third   \n",
       "\n",
       "       who  adult_male deck  embark_town alive  alone aduld/child category1  \\\n",
       "886    man        True  NaN  Southampton    no   True       ADULT      male   \n",
       "887  woman       False    B  Southampton   yes   True       CHILD     CHILD   \n",
       "888  woman       False  NaN  Southampton    no  False       CHILD     CHILD   \n",
       "889    man        True    C    Cherbourg   yes   True       ADULT      male   \n",
       "890    man        True  NaN   Queenstown    no   True       ADULT      male   \n",
       "\n",
       "                                             category2  \n",
       "886  0        male\\n1      female\\n2      female\\n3...  \n",
       "887  0        male\\n1      female\\n2      female\\n3...  \n",
       "888  0        male\\n1      female\\n2      female\\n3...  \n",
       "889  0        male\\n1      female\\n2      female\\n3...  \n",
       "890  0        male\\n1      female\\n2      female\\n3...  "
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[\"aduld/child\"] = titanic.apply(lambda r: \"ADULT\" if r.age > 20 else \"CHILD\", axis = 1)\n",
    "titanic.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "adbf637b",
   "metadata": {},
   "outputs": [
    {
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       "      <td>S</td>\n",
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       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "      <td>ADULT</td>\n",
       "      <td>male</td>\n",
       "      <td>0        male\\n1      female\\n2      female\\n3...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "0           0       3    male  22.0      1      0   7.2500        S   Third   \n",
       "1           1       1  female  38.0      1      0  71.2833        C   First   \n",
       "2           1       3  female  26.0      0      0   7.9250        S   Third   \n",
       "3           1       1  female  35.0      1      0  53.1000        S   First   \n",
       "4           0       3    male  35.0      0      0   8.0500        S   Third   \n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
       "886         0       2    male  27.0      0      0  13.0000        S  Second   \n",
       "887         1       1  female  19.0      0      0  30.0000        S   First   \n",
       "888         0       3  female   NaN      1      2  23.4500        S   Third   \n",
       "889         1       1    male  26.0      0      0  30.0000        C   First   \n",
       "890         0       3    male  32.0      0      0   7.7500        Q   Third   \n",
       "\n",
       "       who  adult_male deck  embark_town alive  alone aduld/child category1  \\\n",
       "0      man        True  NaN  Southampton    no  False       ADULT      male   \n",
       "1    woman       False    C    Cherbourg   yes  False       ADULT    female   \n",
       "2    woman       False  NaN  Southampton   yes   True       ADULT    female   \n",
       "3    woman       False    C  Southampton   yes  False       ADULT    female   \n",
       "4      man        True  NaN  Southampton    no   True       ADULT      male   \n",
       "..     ...         ...  ...          ...   ...    ...         ...       ...   \n",
       "886    man        True  NaN  Southampton    no   True       ADULT      male   \n",
       "887  woman       False    B  Southampton   yes   True       CHILD     CHILD   \n",
       "888  woman       False  NaN  Southampton    no  False       CHILD     CHILD   \n",
       "889    man        True    C    Cherbourg   yes   True       ADULT      male   \n",
       "890    man        True  NaN   Queenstown    no   True       ADULT      male   \n",
       "\n",
       "                                             category2  \n",
       "0    0        male\\n1      female\\n2      female\\n3...  \n",
       "1    0        male\\n1      female\\n2      female\\n3...  \n",
       "2    0        male\\n1      female\\n2      female\\n3...  \n",
       "3    0        male\\n1      female\\n2      female\\n3...  \n",
       "4    0        male\\n1      female\\n2      female\\n3...  \n",
       "..                                                 ...  \n",
       "886  0        male\\n1      female\\n2      female\\n3...  \n",
       "887  0        male\\n1      female\\n2      female\\n3...  \n",
       "888  0        male\\n1      female\\n2      female\\n3...  \n",
       "889  0        male\\n1      female\\n2      female\\n3...  \n",
       "890  0        male\\n1      female\\n2      female\\n3...  \n",
       "\n",
       "[891 rows x 18 columns]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[\"category1\"] = titanic.apply(lambda f: f.sex if f.age > 20 else \"CHILD\", axis = 1)\n",
    "titanic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "da2ac069",
   "metadata": {},
   "outputs": [
    {
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       "   A  B  C\n",
       "0  1  2  1\n",
       "1  3  3  5\n",
       "2  4  1  2\n",
       "3  3  2  4\n",
       "4  4  3  4"
      ]
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     "execution_count": 108,
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   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "2fa18185",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanicMeanAge = titanic[\"age\"].mean().astype(int)\n",
    "titanicMeanAge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "e8e7021b",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "0           0       3    male  22.0      1      0   7.2500        S   Third   \n",
       "1           1       1  female  38.0      1      0  71.2833        C   First   \n",
       "2           1       3  female  26.0      0      0   7.9250        S   Third   \n",
       "3           1       1  female  35.0      1      0  53.1000        S   First   \n",
       "4           0       3    male  35.0      0      0   8.0500        S   Third   \n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
       "886         0       2    male  27.0      0      0  13.0000        S  Second   \n",
       "887         1       1  female  19.0      0      0  30.0000        S   First   \n",
       "888         0       3  female  29.0      1      2  23.4500        S   Third   \n",
       "889         1       1    male  26.0      0      0  30.0000        C   First   \n",
       "890         0       3    male  32.0      0      0   7.7500        Q   Third   \n",
       "\n",
       "       who  adult_male deck  embark_town alive  alone aduld/child category1  \\\n",
       "0      man        True  NaN  Southampton    no  False       ADULT      male   \n",
       "1    woman       False    C    Cherbourg   yes  False       ADULT    female   \n",
       "2    woman       False  NaN  Southampton   yes   True       ADULT    female   \n",
       "3    woman       False    C  Southampton   yes  False       ADULT    female   \n",
       "4      man        True  NaN  Southampton    no   True       ADULT      male   \n",
       "..     ...         ...  ...          ...   ...    ...         ...       ...   \n",
       "886    man        True  NaN  Southampton    no   True       ADULT      male   \n",
       "887  woman       False    B  Southampton   yes   True       CHILD     CHILD   \n",
       "888  woman       False  NaN  Southampton    no  False       CHILD     CHILD   \n",
       "889    man        True    C    Cherbourg   yes   True       ADULT      male   \n",
       "890    man        True  NaN   Queenstown    no   True       ADULT      male   \n",
       "\n",
       "      category2  \n",
       "0      male22.0  \n",
       "1    female38.0  \n",
       "2    female26.0  \n",
       "3    female35.0  \n",
       "4      male35.0  \n",
       "..          ...  \n",
       "886    male27.0  \n",
       "887  female19.0  \n",
       "888   femalenan  \n",
       "889    male26.0  \n",
       "890    male32.0  \n",
       "\n",
       "[891 rows x 18 columns]"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[\"age\"] = titanic[\"age\"].fillna(titanicMeanAge)\n",
    "titanic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "0184097a",
   "metadata": {},
   "outputs": [
    {
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       "   A  B  C\n",
       "0  1  2  1\n",
       "1  3  3  5\n",
       "2  4  1  2\n",
       "3  3  2  4\n",
       "4  4  3  4"
      ]
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     "execution_count": 161,
     "metadata": {},
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    "df3.apply(pd.value_counts).fillna(0).astype(int)\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "561ba67b",
   "metadata": {},
   "outputs": [
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       "      <td>male22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "      <td>ADULT</td>\n",
       "      <td>female</td>\n",
       "      <td>female38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "      <td>ADULT</td>\n",
       "      <td>female</td>\n",
       "      <td>female26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "      <td>ADULT</td>\n",
       "      <td>female</td>\n",
       "      <td>female35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "      <td>ADULT</td>\n",
       "      <td>male</td>\n",
       "      <td>male35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "      <td>ADULT</td>\n",
       "      <td>male</td>\n",
       "      <td>male27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>B</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "      <td>CHILD</td>\n",
       "      <td>CHILD</td>\n",
       "      <td>female19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "      <td>CHILD</td>\n",
       "      <td>CHILD</td>\n",
       "      <td>female29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "      <td>ADULT</td>\n",
       "      <td>male</td>\n",
       "      <td>male26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "      <td>ADULT</td>\n",
       "      <td>male</td>\n",
       "      <td>male32.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex  age  sibsp  parch     fare embarked   class  \\\n",
       "0           0       3    male   22      1      0   7.2500        S   Third   \n",
       "1           1       1  female   38      1      0  71.2833        C   First   \n",
       "2           1       3  female   26      0      0   7.9250        S   Third   \n",
       "3           1       1  female   35      1      0  53.1000        S   First   \n",
       "4           0       3    male   35      0      0   8.0500        S   Third   \n",
       "..        ...     ...     ...  ...    ...    ...      ...      ...     ...   \n",
       "886         0       2    male   27      0      0  13.0000        S  Second   \n",
       "887         1       1  female   19      0      0  30.0000        S   First   \n",
       "888         0       3  female   29      1      2  23.4500        S   Third   \n",
       "889         1       1    male   26      0      0  30.0000        C   First   \n",
       "890         0       3    male   32      0      0   7.7500        Q   Third   \n",
       "\n",
       "       who  adult_male deck  embark_town alive  alone aduld/child category1  \\\n",
       "0      man        True  NaN  Southampton    no  False       ADULT      male   \n",
       "1    woman       False    C    Cherbourg   yes  False       ADULT    female   \n",
       "2    woman       False  NaN  Southampton   yes   True       ADULT    female   \n",
       "3    woman       False    C  Southampton   yes  False       ADULT    female   \n",
       "4      man        True  NaN  Southampton    no   True       ADULT      male   \n",
       "..     ...         ...  ...          ...   ...    ...         ...       ...   \n",
       "886    man        True  NaN  Southampton    no   True       ADULT      male   \n",
       "887  woman       False    B  Southampton   yes   True       CHILD     CHILD   \n",
       "888  woman       False  NaN  Southampton    no  False       CHILD     CHILD   \n",
       "889    man        True    C    Cherbourg   yes   True       ADULT      male   \n",
       "890    man        True  NaN   Queenstown    no   True       ADULT      male   \n",
       "\n",
       "      category2  \n",
       "0      male22.0  \n",
       "1    female38.0  \n",
       "2    female26.0  \n",
       "3    female35.0  \n",
       "4      male35.0  \n",
       "..          ...  \n",
       "886    male27.0  \n",
       "887  female19.0  \n",
       "888  female29.0  \n",
       "889    male26.0  \n",
       "890    male32.0  \n",
       "\n",
       "[891 rows x 18 columns]"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[\"age\"] = titanic[\"age\"].astype(int)\n",
    "titanic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "d7f59ea4",
   "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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "      <th>aduld/child</th>\n",
       "      <th>category1</th>\n",
       "      <th>category2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
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       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "      <td>ADULT</td>\n",
       "      <td>male</td>\n",
       "      <td>male22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "      <td>ADULT</td>\n",
       "      <td>female</td>\n",
       "      <td>female38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "      <td>ADULT</td>\n",
       "      <td>female</td>\n",
       "      <td>female26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "      <td>ADULT</td>\n",
       "      <td>female</td>\n",
       "      <td>female35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "      <td>ADULT</td>\n",
       "      <td>male</td>\n",
       "      <td>male35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "      <td>ADULT</td>\n",
       "      <td>male</td>\n",
       "      <td>male27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>B</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "      <td>CHILD</td>\n",
       "      <td>CHILD</td>\n",
       "      <td>female19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "      <td>CHILD</td>\n",
       "      <td>CHILD</td>\n",
       "      <td>female29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "      <td>ADULT</td>\n",
       "      <td>male</td>\n",
       "      <td>male26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "      <td>ADULT</td>\n",
       "      <td>male</td>\n",
       "      <td>male32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex  age  sibsp  parch     fare embarked   class  \\\n",
       "0           0       3    male   22      1      0   7.2500        S   Third   \n",
       "1           1       1  female   38      1      0  71.2833        C   First   \n",
       "2           1       3  female   26      0      0   7.9250        S   Third   \n",
       "3           1       1  female   35      1      0  53.1000        S   First   \n",
       "4           0       3    male   35      0      0   8.0500        S   Third   \n",
       "..        ...     ...     ...  ...    ...    ...      ...      ...     ...   \n",
       "886         0       2    male   27      0      0  13.0000        S  Second   \n",
       "887         1       1  female   19      0      0  30.0000        S   First   \n",
       "888         0       3  female   29      1      2  23.4500        S   Third   \n",
       "889         1       1    male   26      0      0  30.0000        C   First   \n",
       "890         0       3    male   32      0      0   7.7500        Q   Third   \n",
       "\n",
       "       who  adult_male deck  embark_town alive  alone aduld/child category1  \\\n",
       "0      man        True  NaN  Southampton    no  False       ADULT      male   \n",
       "1    woman       False    C    Cherbourg   yes  False       ADULT    female   \n",
       "2    woman       False  NaN  Southampton   yes   True       ADULT    female   \n",
       "3    woman       False    C  Southampton   yes  False       ADULT    female   \n",
       "4      man        True  NaN  Southampton    no   True       ADULT      male   \n",
       "..     ...         ...  ...          ...   ...    ...         ...       ...   \n",
       "886    man        True  NaN  Southampton    no   True       ADULT      male   \n",
       "887  woman       False    B  Southampton   yes   True       CHILD     CHILD   \n",
       "888  woman       False  NaN  Southampton    no  False       CHILD     CHILD   \n",
       "889    man        True    C    Cherbourg   yes   True       ADULT      male   \n",
       "890    man        True  NaN   Queenstown    no   True       ADULT      male   \n",
       "\n",
       "    category2  \n",
       "0      male22  \n",
       "1    female38  \n",
       "2    female26  \n",
       "3    female35  \n",
       "4      male35  \n",
       "..        ...  \n",
       "886    male27  \n",
       "887  female19  \n",
       "888  female29  \n",
       "889    male26  \n",
       "890    male32  \n",
       "\n",
       "[891 rows x 18 columns]"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[\"category2\"] = titanic.apply(lambda c: c.sex + str(c.age), axis = 1)\n",
    "titanic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "895a69a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "ages = [0, 2, 10, 21, 23, 37, 31, 61, 20, 41, 32, 101]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "d9bf36e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[NaN, '미성년자', '미성년자', '청년', '청년', ..., '장년', '미성년자', '중년', '중년', NaN]\n",
       "Length: 12\n",
       "Categories (5, object): ['미성년자' < '청년' < '중년' < '장년' < '노년']"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = [1, 20, 30, 50, 70, 100]\n",
    "labels = [\"미성년자\", \"청년\", \"중년\", \"장년\", \"노년\"]\n",
    "cats = pd.cut(ages, bins, labels=labels)\n",
    "cats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "c2fa161b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.arrays.categorical.Categorical"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(cats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "ecc7a1a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['미성년자', '청년', '중년', '장년', '노년'], dtype='object')"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats.categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "ac273a28",
   "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>ages</th>\n",
       "      <th>age_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>미성년자</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>미성년자</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21</td>\n",
       "      <td>청년</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23</td>\n",
       "      <td>청년</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>37</td>\n",
       "      <td>중년</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>31</td>\n",
       "      <td>중년</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>61</td>\n",
       "      <td>장년</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20</td>\n",
       "      <td>미성년자</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>41</td>\n",
       "      <td>중년</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>32</td>\n",
       "      <td>중년</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>101</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ages age_cat\n",
       "0      0     NaN\n",
       "1      2    미성년자\n",
       "2     10    미성년자\n",
       "3     21      청년\n",
       "4     23      청년\n",
       "5     37      중년\n",
       "6     31      중년\n",
       "7     61      장년\n",
       "8     20    미성년자\n",
       "9     41      중년\n",
       "10    32      중년\n",
       "11   101     NaN"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = pd.DataFrame(ages, columns=[\"ages\"])\n",
    "df4[\"age_cat\"] = pd.cut(df4.ages, bins, labels=labels)\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "11a97cb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       nan0\n",
       "1      미성년자2\n",
       "2     미성년자10\n",
       "3       청년21\n",
       "4       청년23\n",
       "5       중년37\n",
       "6       중년31\n",
       "7       장년61\n",
       "8     미성년자20\n",
       "9       중년41\n",
       "10      중년32\n",
       "11    nan101\n",
       "dtype: object"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.age_cat.astype(str) + df4.ages.astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "215d0b6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Q2', 'Q1', 'Q2', 'Q3', 'Q1', ..., 'Q1', 'Q1', 'Q4', 'Q4', 'Q2']\n",
       "Length: 1000\n",
       "Categories (4, object): ['Q1' < 'Q2' < 'Q3' < 'Q4']"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.random.randn(1000)\n",
    "cats = pd.qcut(data, 4, labels=[\"Q1\", \"Q2\", \"Q3\", \"Q4\"])\n",
    "cats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "ce458143",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Q1    250\n",
       "Q2    250\n",
       "Q3    250\n",
       "Q4    250\n",
       "dtype: int64"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(cats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "4ea850f1",
   "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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "      <th>aduld/child</th>\n",
       "      <th>category1</th>\n",
       "      <th>category2</th>\n",
       "      <th>cats</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "      <td>ADULT</td>\n",
       "      <td>male</td>\n",
       "      <td>male22</td>\n",
       "      <td>청년</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "      <td>ADULT</td>\n",
       "      <td>female</td>\n",
       "      <td>female38</td>\n",
       "      <td>중년</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "      <td>ADULT</td>\n",
       "      <td>female</td>\n",
       "      <td>female26</td>\n",
       "      <td>청년</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "      <td>ADULT</td>\n",
       "      <td>female</td>\n",
       "      <td>female35</td>\n",
       "      <td>중년</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "      <td>ADULT</td>\n",
       "      <td>male</td>\n",
       "      <td>male35</td>\n",
       "      <td>중년</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "      <td>ADULT</td>\n",
       "      <td>male</td>\n",
       "      <td>male27</td>\n",
       "      <td>청년</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>B</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "      <td>CHILD</td>\n",
       "      <td>CHILD</td>\n",
       "      <td>female19</td>\n",
       "      <td>미성년자</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "      <td>CHILD</td>\n",
       "      <td>CHILD</td>\n",
       "      <td>female29</td>\n",
       "      <td>청년</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "      <td>ADULT</td>\n",
       "      <td>male</td>\n",
       "      <td>male26</td>\n",
       "      <td>청년</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "      <td>ADULT</td>\n",
       "      <td>male</td>\n",
       "      <td>male32</td>\n",
       "      <td>중년</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex  age  sibsp  parch     fare embarked   class  \\\n",
       "0           0       3    male   22      1      0   7.2500        S   Third   \n",
       "1           1       1  female   38      1      0  71.2833        C   First   \n",
       "2           1       3  female   26      0      0   7.9250        S   Third   \n",
       "3           1       1  female   35      1      0  53.1000        S   First   \n",
       "4           0       3    male   35      0      0   8.0500        S   Third   \n",
       "..        ...     ...     ...  ...    ...    ...      ...      ...     ...   \n",
       "886         0       2    male   27      0      0  13.0000        S  Second   \n",
       "887         1       1  female   19      0      0  30.0000        S   First   \n",
       "888         0       3  female   29      1      2  23.4500        S   Third   \n",
       "889         1       1    male   26      0      0  30.0000        C   First   \n",
       "890         0       3    male   32      0      0   7.7500        Q   Third   \n",
       "\n",
       "       who  adult_male deck  embark_town alive  alone aduld/child category1  \\\n",
       "0      man        True  NaN  Southampton    no  False       ADULT      male   \n",
       "1    woman       False    C    Cherbourg   yes  False       ADULT    female   \n",
       "2    woman       False  NaN  Southampton   yes   True       ADULT    female   \n",
       "3    woman       False    C  Southampton   yes  False       ADULT    female   \n",
       "4      man        True  NaN  Southampton    no   True       ADULT      male   \n",
       "..     ...         ...  ...          ...   ...    ...         ...       ...   \n",
       "886    man        True  NaN  Southampton    no   True       ADULT      male   \n",
       "887  woman       False    B  Southampton   yes   True       CHILD     CHILD   \n",
       "888  woman       False  NaN  Southampton    no  False       CHILD     CHILD   \n",
       "889    man        True    C    Cherbourg   yes   True       ADULT      male   \n",
       "890    man        True  NaN   Queenstown    no   True       ADULT      male   \n",
       "\n",
       "    category2  cats  \n",
       "0      male22    청년  \n",
       "1    female38    중년  \n",
       "2    female26    청년  \n",
       "3    female35    중년  \n",
       "4      male35    중년  \n",
       "..        ...   ...  \n",
       "886    male27    청년  \n",
       "887  female19  미성년자  \n",
       "888  female29    청년  \n",
       "889    male26    청년  \n",
       "890    male32    중년  \n",
       "\n",
       "[891 rows x 19 columns]"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = [1, 20, 30, 50, 70, 100]\n",
    "labels = [\"미성년자\", \"청년\", \"중년\", \"장년\", \"노년\"]\n",
    "cats = pd.cut(titanic[\"age\"], bins, labels = labels)\n",
    "titanic[\"cats\"] = cats\n",
    "titanic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "8019c85f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.186307519640853"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(cats)[\"미성년자\"] / len(cats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "cabf04a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.45791\n",
      "0.26824\n",
      "0.18631\n",
      "0.06734\n",
      "0.00449\n"
     ]
    }
   ],
   "source": [
    "for i in pd.value_counts(cats):\n",
    "    print(round((i/len(cats)),5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "3a046567",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "      <th>aduld/child</th>\n",
       "      <th>category1</th>\n",
       "      <th>category2</th>\n",
       "      <th>cats</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "      <td>ADULT</td>\n",
       "      <td>male</td>\n",
       "      <td>male27</td>\n",
       "      <td>청년</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.00</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>B</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "      <td>CHILD</td>\n",
       "      <td>CHILD</td>\n",
       "      <td>female19</td>\n",
       "      <td>미성년자</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.45</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "      <td>CHILD</td>\n",
       "      <td>CHILD</td>\n",
       "      <td>female29</td>\n",
       "      <td>청년</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.00</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "      <td>ADULT</td>\n",
       "      <td>male</td>\n",
       "      <td>male26</td>\n",
       "      <td>청년</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.75</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "      <td>ADULT</td>\n",
       "      <td>male</td>\n",
       "      <td>male32</td>\n",
       "      <td>중년</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex  age  sibsp  parch   fare embarked   class  \\\n",
       "886         0       2    male   27      0      0  13.00        S  Second   \n",
       "887         1       1  female   19      0      0  30.00        S   First   \n",
       "888         0       3  female   29      1      2  23.45        S   Third   \n",
       "889         1       1    male   26      0      0  30.00        C   First   \n",
       "890         0       3    male   32      0      0   7.75        Q   Third   \n",
       "\n",
       "       who  adult_male deck  embark_town alive  alone aduld/child category1  \\\n",
       "886    man        True  NaN  Southampton    no   True       ADULT      male   \n",
       "887  woman       False    B  Southampton   yes   True       CHILD     CHILD   \n",
       "888  woman       False  NaN  Southampton    no  False       CHILD     CHILD   \n",
       "889    man        True    C    Cherbourg   yes   True       ADULT      male   \n",
       "890    man        True  NaN   Queenstown    no   True       ADULT      male   \n",
       "\n",
       "    category2  cats  \n",
       "886    male27    청년  \n",
       "887  female19  미성년자  \n",
       "888  female29    청년  \n",
       "889    male26    청년  \n",
       "890    male32    중년  "
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "b536382e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
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       "      <th>category2</th>\n",
       "      <th>cats</th>\n",
       "      <th>category3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>male</td>\n",
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       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
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       "      <td>True</td>\n",
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       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "      <td>ADULT</td>\n",
       "      <td>male</td>\n",
       "      <td>male22</td>\n",
       "      <td>청년</td>\n",
       "      <td>청년남성</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "      <td>ADULT</td>\n",
       "      <td>female</td>\n",
       "      <td>female38</td>\n",
       "      <td>중년</td>\n",
       "      <td>중년여성</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
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       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "      <td>ADULT</td>\n",
       "      <td>female</td>\n",
       "      <td>female26</td>\n",
       "      <td>청년</td>\n",
       "      <td>청년여성</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
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       "      <td>False</td>\n",
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       "      <td>female</td>\n",
       "      <td>female35</td>\n",
       "      <td>중년</td>\n",
       "      <td>중년여성</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "      <td>ADULT</td>\n",
       "      <td>male</td>\n",
       "      <td>male35</td>\n",
       "      <td>중년</td>\n",
       "      <td>중년남성</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "      <td>ADULT</td>\n",
       "      <td>male</td>\n",
       "      <td>male27</td>\n",
       "      <td>청년</td>\n",
       "      <td>청년남성</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>B</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "      <td>CHILD</td>\n",
       "      <td>CHILD</td>\n",
       "      <td>female19</td>\n",
       "      <td>미성년자</td>\n",
       "      <td>미성년자</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "      <td>CHILD</td>\n",
       "      <td>CHILD</td>\n",
       "      <td>female29</td>\n",
       "      <td>청년</td>\n",
       "      <td>청년여성</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "      <td>ADULT</td>\n",
       "      <td>male</td>\n",
       "      <td>male26</td>\n",
       "      <td>청년</td>\n",
       "      <td>청년남성</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "      <td>ADULT</td>\n",
       "      <td>male</td>\n",
       "      <td>male32</td>\n",
       "      <td>중년</td>\n",
       "      <td>중년남성</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex  age  sibsp  parch     fare embarked   class  \\\n",
       "0           0       3    male   22      1      0   7.2500        S   Third   \n",
       "1           1       1  female   38      1      0  71.2833        C   First   \n",
       "2           1       3  female   26      0      0   7.9250        S   Third   \n",
       "3           1       1  female   35      1      0  53.1000        S   First   \n",
       "4           0       3    male   35      0      0   8.0500        S   Third   \n",
       "..        ...     ...     ...  ...    ...    ...      ...      ...     ...   \n",
       "886         0       2    male   27      0      0  13.0000        S  Second   \n",
       "887         1       1  female   19      0      0  30.0000        S   First   \n",
       "888         0       3  female   29      1      2  23.4500        S   Third   \n",
       "889         1       1    male   26      0      0  30.0000        C   First   \n",
       "890         0       3    male   32      0      0   7.7500        Q   Third   \n",
       "\n",
       "       who  adult_male deck  embark_town alive  alone aduld/child category1  \\\n",
       "0      man        True  NaN  Southampton    no  False       ADULT      male   \n",
       "1    woman       False    C    Cherbourg   yes  False       ADULT    female   \n",
       "2    woman       False  NaN  Southampton   yes   True       ADULT    female   \n",
       "3    woman       False    C  Southampton   yes  False       ADULT    female   \n",
       "4      man        True  NaN  Southampton    no   True       ADULT      male   \n",
       "..     ...         ...  ...          ...   ...    ...         ...       ...   \n",
       "886    man        True  NaN  Southampton    no   True       ADULT      male   \n",
       "887  woman       False    B  Southampton   yes   True       CHILD     CHILD   \n",
       "888  woman       False  NaN  Southampton    no  False       CHILD     CHILD   \n",
       "889    man        True    C    Cherbourg   yes   True       ADULT      male   \n",
       "890    man        True  NaN   Queenstown    no   True       ADULT      male   \n",
       "\n",
       "    category2  cats category3  \n",
       "0      male22    청년      청년남성  \n",
       "1    female38    중년      중년여성  \n",
       "2    female26    청년      청년여성  \n",
       "3    female35    중년      중년여성  \n",
       "4      male35    중년      중년남성  \n",
       "..        ...   ...       ...  \n",
       "886    male27    청년      청년남성  \n",
       "887  female19  미성년자      미성년자  \n",
       "888  female29    청년      청년여성  \n",
       "889    male26    청년      청년남성  \n",
       "890    male32    중년      중년남성  \n",
       "\n",
       "[891 rows x 20 columns]"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[\"category3\"] = titanic.apply(lambda a: \"미성년자\" if a.age < 20 else a.cats + (\"남성\" if a.sex == \"male\" else \"여성\"), axis = 1)\n",
    "titanic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd39ae3a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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