4장_판다스_데이터_분석_(4.4리뷰).ipynb

{
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  {
   "cell_type": "code",
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   "outputs": [
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     "data": {
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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": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "titanic = sns.load_dataset(\"titanic\")\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "0a3d25af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "630    80.0\n",
       "851    74.0\n",
       "493    71.0\n",
       "96     71.0\n",
       "116    70.5\n",
       "       ... \n",
       "859     NaN\n",
       "863     NaN\n",
       "868     NaN\n",
       "878     NaN\n",
       "888     NaN\n",
       "Name: age, Length: 891, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.age.sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2bae21b5",
   "metadata": {},
   "outputs": [
    {
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       "</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",
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       "      <th>803</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.5167</td>\n",
       "      <td>C</td>\n",
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       "      <td>child</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
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       "      <th>755</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14.5000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>child</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>19.2583</td>\n",
       "      <td>C</td>\n",
       "      <td>Third</td>\n",
       "      <td>child</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0.75</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>19.2583</td>\n",
       "      <td>C</td>\n",
       "      <td>Third</td>\n",
       "      <td>child</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>child</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>859</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>C</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>69.5500</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>868</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.5000</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>878</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8958</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>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",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "803         1       3    male  0.42      0      1   8.5167        C   Third   \n",
       "755         1       2    male  0.67      1      1  14.5000        S  Second   \n",
       "644         1       3  female  0.75      2      1  19.2583        C   Third   \n",
       "469         1       3  female  0.75      2      1  19.2583        C   Third   \n",
       "78          1       2    male  0.83      0      2  29.0000        S  Second   \n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
       "859         0       3    male   NaN      0      0   7.2292        C   Third   \n",
       "863         0       3  female   NaN      8      2  69.5500        S   Third   \n",
       "868         0       3    male   NaN      0      0   9.5000        S   Third   \n",
       "878         0       3    male   NaN      0      0   7.8958        S   Third   \n",
       "888         0       3  female   NaN      1      2  23.4500        S   Third   \n",
       "\n",
       "       who  adult_male deck  embark_town alive  alone  \n",
       "803  child       False  NaN    Cherbourg   yes  False  \n",
       "755  child       False  NaN  Southampton   yes  False  \n",
       "644  child       False  NaN    Cherbourg   yes  False  \n",
       "469  child       False  NaN    Cherbourg   yes  False  \n",
       "78   child       False  NaN  Southampton   yes  False  \n",
       "..     ...         ...  ...          ...   ...    ...  \n",
       "859    man        True  NaN    Cherbourg    no   True  \n",
       "863  woman       False  NaN  Southampton    no  False  \n",
       "868    man        True  NaN  Southampton    no   True  \n",
       "878    man        True  NaN  Southampton    no   True  \n",
       "888  woman       False  NaN  Southampton    no  False  \n",
       "\n",
       "[891 rows x 15 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.sort_values(by = \"age\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "44b640b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>class</th>\n",
       "      <th>alive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Third</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>First</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Third</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>First</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>Third</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Second</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>First</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Third</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>First</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>Third</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        sex   age   class alive\n",
       "0      male  22.0   Third    no\n",
       "1    female  38.0   First   yes\n",
       "2    female  26.0   Third   yes\n",
       "3    female  35.0   First   yes\n",
       "4      male  35.0   Third    no\n",
       "..      ...   ...     ...   ...\n",
       "886    male  27.0  Second    no\n",
       "887  female  19.0   First   yes\n",
       "888  female   NaN   Third    no\n",
       "889    male  26.0   First   yes\n",
       "890    male  32.0   Third    no\n",
       "\n",
       "[891 rows x 4 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[[\"sex\", \"age\", \"class\", \"alive\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "738c0c45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      577\n",
       "female    314\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic['sex'].sort_values().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c53bfdc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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",
       "Name: age, Length: 88, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[\"age\"].sort_values().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "44f28d25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Third     491\n",
       "First     216\n",
       "Second    184\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic['class'].sort_values().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9a684ebd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     549\n",
       "yes    342\n",
       "Name: alive, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic['alive'].sort_values().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "02c47827",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.69911764705882"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[\"age\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "7ae9f4f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27.915708812260537"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.loc[titanic.sex == \"female\"][\"age\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "b1cce00d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34.61176470588235"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.loc[(titanic['class'] == \"First\") & (titanic.sex == \"female\")]['age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "a501300c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "536c7473",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
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       "      <td>female</td>\n",
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       "      <td>0</td>\n",
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       "      <td>S</td>\n",
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       "      <td>woman</td>\n",
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       "      <td>B</td>\n",
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       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.45</td>\n",
       "      <td>S</td>\n",
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       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
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       "      <th>889</th>\n",
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       "      <td>True</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "      <td>male</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>7.75</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>male</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 category1  \n",
       "886    man        True  NaN  Southampton    no   True      male  \n",
       "887  woman       False    B  Southampton   yes   True     child  \n",
       "888  woman       False  NaN  Southampton    no  False     child  \n",
       "889    man        True    C    Cherbourg   yes   True      male  \n",
       "890    man        True  NaN   Queenstown    no   True      male  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic['category1'] = titanic.apply(lambda x: x.sex if x.age > 20 else \"child\", axis = 1)\n",
    "titanic.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "1e3174f0",
   "metadata": {},
   "outputs": [
    {
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       "      <td>19</td>\n",
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       "      <td>0</td>\n",
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       "      <td>True</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "      <td>male</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",
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       "      <td>True</td>\n",
       "      <td>male</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 category1  \n",
       "886    man        True  NaN  Southampton    no   True      male  \n",
       "887  woman       False    B  Southampton   yes   True     child  \n",
       "888  woman       False  NaN  Southampton    no  False     child  \n",
       "889    man        True    C    Cherbourg   yes   True      male  \n",
       "890    man        True  NaN   Queenstown    no   True      male  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.age = titanic.age.fillna(titanic.age.mean())\n",
    "titanic.age = titanic.age.astype(int)\n",
    "titanic.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "06089a47",
   "metadata": {},
   "outputs": [
    {
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       "      <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>male</td>\n",
       "      <td>male26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
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       "      <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>male</td>\n",
       "      <td>male32</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 category1 category2  \n",
       "886    man        True  NaN  Southampton    no   True      male    male27  \n",
       "887  woman       False    B  Southampton   yes   True     child  female19  \n",
       "888  woman       False  NaN  Southampton    no  False     child  female29  \n",
       "889    man        True    C    Cherbourg   yes   True      male    male26  \n",
       "890    man        True  NaN   Queenstown    no   True      male    male32  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.age = titanic.age.astype(str)\n",
    "titanic['category2'] = titanic.sex + titanic.age\n",
    "titanic.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "f686a2b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        청년\n",
       "1        중년\n",
       "2        청년\n",
       "3        중년\n",
       "4        중년\n",
       "       ... \n",
       "886      청년\n",
       "887    미성년자\n",
       "888      청년\n",
       "889      청년\n",
       "890      중년\n",
       "Name: age, Length: 891, dtype: category\n",
       "Categories (5, object): ['미성년자' < '청년' < '중년' < '장년' < '노년']"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.age = titanic.age.astype(int)\n",
    "\n",
    "bins = [1, 20, 30, 50, 70, 100]\n",
    "labels = [\"미성년자\", \"청년\", \"중년\", \"장년\", \"노년\"]\n",
    "cats = pd.cut(titanic.age, bins, labels = labels)\n",
    "cats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "958e80d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "청년      0.457912\n",
       "중년      0.268238\n",
       "미성년자    0.186308\n",
       "장년      0.067340\n",
       "노년      0.004489\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats.value_counts() / len(cats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "1722401c",
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic[\"cats\"] = cats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "61bd14a1",
   "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>category1</th>\n",
       "      <th>category2</th>\n",
       "      <th>category3</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>male</td>\n",
       "      <td>male27</td>\n",
       "      <td>청년남성</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>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>23.45</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>female29</td>\n",
       "      <td>청년여성</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>male</td>\n",
       "      <td>male26</td>\n",
       "      <td>청년남성</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>male</td>\n",
       "      <td>male32</td>\n",
       "      <td>중년남성</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 category1 category2  \\\n",
       "886    man        True  NaN  Southampton    no   True      male    male27   \n",
       "887  woman       False    B  Southampton   yes   True     child  female19   \n",
       "888  woman       False  NaN  Southampton    no  False     child  female29   \n",
       "889    man        True    C    Cherbourg   yes   True      male    male26   \n",
       "890    man        True  NaN   Queenstown    no   True      male    male32   \n",
       "\n",
       "    category3  cats  \n",
       "886      청년남성    청년  \n",
       "887      미성년자  미성년자  \n",
       "888      청년여성    청년  \n",
       "889      청년남성    청년  \n",
       "890      중년남성    중년  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic['category3'] = titanic.apply(lambda x: \"미성년자\" if x.age < 20 \n",
    "                                     else x.cats + \"남성\" if x.sex == \"male\"\n",
    "                                     else x.cats + \"여성\", axis = 1)\n",
    "titanic.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9ebe56f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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