ch05 Pandas isna fillna dropna quantile.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "182217fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ffd7381b",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sepal_Length</th>\n",
       "      <th>Sepal_Width</th>\n",
       "      <th>Petal_Length</th>\n",
       "      <th>Petal_Width</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "   Sepal_Length  Sepal_Width  Petal_Length  Petal_Width Species\n",
       "0           5.1          NaN           NaN          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2     NaN\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2     NaN"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"강의자료/실습파일/iris_missing.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1657f00b",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>Sepal_Length</th>\n",
       "      <th>Sepal_Width</th>\n",
       "      <th>Petal_Length</th>\n",
       "      <th>Petal_Width</th>\n",
       "      <th>Species</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sepal_Length  Sepal_Width  Petal_Length  Petal_Width Species\n",
       "0           5.1          NaN           NaN          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2     NaN\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2     NaN\n",
       "5           5.4          3.9           1.7          0.4  setosa\n",
       "6           NaN          NaN           NaN          NaN  setosa"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_na = df.head(7)\n",
    "df_na"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5aa212a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "5    False\n",
       "6     True\n",
       "Name: Sepal_Length, dtype: bool"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_na[\"Sepal_Length\"].isna() # 결측치 = True로 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bd169969",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1     True\n",
       "2     True\n",
       "3     True\n",
       "4     True\n",
       "5     True\n",
       "6    False\n",
       "Name: Sepal_Length, dtype: bool"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_na[\"Sepal_Length\"].notna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0cd38bb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_na[\"Sepal_Length\"].isna().sum()  #결측치 개수 (True = 1 이므로...)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6e9da9bd",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>Sepal_Length</th>\n",
       "      <th>Sepal_Width</th>\n",
       "      <th>Petal_Length</th>\n",
       "      <th>Petal_Width</th>\n",
       "      <th>Species</th>\n",
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       "      <td>True</td>\n",
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       "      <th>5</th>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>6</th>\n",
       "      <td>True</td>\n",
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       "      <td>False</td>\n",
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      ],
      "text/plain": [
       "   Sepal_Length  Sepal_Width  Petal_Length  Petal_Width  Species\n",
       "0         False         True          True        False    False\n",
       "1         False        False         False        False    False\n",
       "2         False        False         False        False     True\n",
       "3         False        False         False        False    False\n",
       "4         False        False         False        False     True\n",
       "5         False        False         False        False    False\n",
       "6          True         True          True         True    False"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_na.isna() # "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1f5ebf66",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sepal_Length    1\n",
       "Sepal_Width     2\n",
       "Petal_Length    2\n",
       "Petal_Width     1\n",
       "Species         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_na.isna().sum()  # 각 변수별 결측치 개수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "372b164d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "1    0\n",
       "2    1\n",
       "3    0\n",
       "4    1\n",
       "5    0\n",
       "6    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_na.isna().sum(axis=1)  # 각 row별 결측치 개수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "27d50d37",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Sepal_Width</th>\n",
       "      <th>Petal_Length</th>\n",
       "      <th>Petal_Width</th>\n",
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       "      <td>setosa</td>\n",
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       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
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       "      <td>setosa</td>\n",
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       "      <th>2</th>\n",
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       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
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       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
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       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>999.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sepal_Length  Sepal_Width  Petal_Length  Petal_Width Species\n",
       "0           5.1          NaN           NaN          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2     NaN\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2     NaN\n",
       "5           5.4          3.9           1.7          0.4  setosa\n",
       "6         999.0          NaN           NaN          NaN  setosa"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_na.fillna(value = {\"Sepal_Length\": 999}) # Sepal_Length 결측치에 999를 채워 넣음."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d61b3edf",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Petal_Length</th>\n",
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       "      <td>setosa</td>\n",
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       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
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       "      <td>setosa</td>\n",
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       "      <td>setosa</td>\n",
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       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>999.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>setosa</td>\n",
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      ],
      "text/plain": [
       "   Sepal_Length  Sepal_Width  Petal_Length  Petal_Width Species\n",
       "0           5.1        999.0           NaN          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2     NaN\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2     NaN\n",
       "5           5.4          3.9           1.7          0.4  setosa\n",
       "6           NaN        999.0           NaN          NaN  setosa"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_na.fillna(value = {\"Sepal_Width\": 999, # 2개 이상 입력할 경우\n",
    "                     \"Petal_Lenth\": 999}) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "783275ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Sepal_Length</th>\n",
       "      <th>Sepal_Width</th>\n",
       "      <th>Petal_Length</th>\n",
       "      <th>Petal_Width</th>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>999.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sepal_Length  Sepal_Width  Petal_Length  Petal_Width Species\n",
       "0           5.1        999.0         999.0          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2     999\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2     999\n",
       "5           5.4          3.9           1.7          0.4  setosa\n",
       "6         999.0        999.0         999.0        999.0  setosa"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_na.fillna(value = 999) # 모든 결측치에 동일한 값 입력 (권장하지 않음)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a0c08090",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.3600000000000003"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_na[\"Sepal_Width\"].mean()  # 결측치를 제외한 평균값 계산"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f9b559e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sepal_Length  Sepal_Width  Petal_Length  Petal_Width Species\n",
       "0           5.1         3.36           NaN          0.2  setosa\n",
       "1           4.9         3.00           1.4          0.2  setosa\n",
       "2           4.7         3.20           1.3          0.2     NaN\n",
       "3           4.6         3.10           1.5          0.2  setosa\n",
       "4           5.0         3.60           1.4          0.2     NaN\n",
       "5           5.4         3.90           1.7          0.4  setosa\n",
       "6           NaN         3.36           NaN          NaN  setosa"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_na.fillna(value = {\"Sepal_Width\": df_na[\"Sepal_Width\"].mean()})  # 결측치에 평균값 입력"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1fa904ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sepal_Length  Sepal_Width  Petal_Length  Petal_Width Species\n",
       "0           5.1          NaN           NaN          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2     NaN\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2     NaN\n",
       "5           5.4          3.9           1.7          0.4  setosa\n",
       "6           NaN          NaN           NaN          NaN  setosa"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_na   # 덮어쓰지 않음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "dad8044d",
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>setosa</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sepal_Length  Sepal_Width  Petal_Length  Petal_Width Species\n",
       "0           5.1         3.36           NaN          0.2  setosa\n",
       "1           4.9         3.00           1.4          0.2  setosa\n",
       "2           4.7         3.20           1.3          0.2     NaN\n",
       "3           4.6         3.10           1.5          0.2  setosa\n",
       "4           5.0         3.60           1.4          0.2     NaN\n",
       "5           5.4         3.90           1.7          0.4  setosa\n",
       "6           NaN         3.36           NaN          NaN  setosa"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_na = df_na.fillna(value = {\"Sepal_Width\": df_na[\"Sepal_Width\"].mean()})  # 덮어쓰기\n",
    "df_na"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6a1dc585",
   "metadata": {},
   "outputs": [
    {
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       "      <td>setosa</td>\n",
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      ],
      "text/plain": [
       "   Sepal_Length  Sepal_Width  Petal_Length  Petal_Width Species\n",
       "0           5.1          NaN           NaN          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2     NaN\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2     NaN\n",
       "5           5.4          3.9           1.7          0.4  setosa\n",
       "6           NaN          NaN           NaN          NaN  setosa\n",
       "7           5.0          3.4           NaN          0.2  setosa"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_na2 = df.head(8)\n",
    "df_na2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bc4d80fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Sepal_Length</th>\n",
       "      <th>Sepal_Width</th>\n",
       "      <th>Petal_Length</th>\n",
       "      <th>Petal_Width</th>\n",
       "      <th>Species</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
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       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sepal_Length  Sepal_Width  Petal_Length  Petal_Width Species\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "5           5.4          3.9           1.7          0.4  setosa"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_na2.dropna()   # 하나라도 결측치가 포함되면 제외한 결과 출력"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4638c0a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sepal_Length</th>\n",
       "      <th>Sepal_Width</th>\n",
       "      <th>Petal_Length</th>\n",
       "      <th>Petal_Width</th>\n",
       "      <th>Species</th>\n",
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       "      <th>0</th>\n",
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       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
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       "      <td>4.9</td>\n",
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       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
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       "      <td>setosa</td>\n",
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       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sepal_Length  Sepal_Width  Petal_Length  Petal_Width Species\n",
       "0           5.1          NaN           NaN          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2     NaN\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2     NaN\n",
       "5           5.4          3.9           1.7          0.4  setosa\n",
       "6           NaN          NaN           NaN          NaN  setosa\n",
       "7           5.0          3.4           NaN          0.2  setosa"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_na2.dropna(how = \"all\")   # 모든 값이 결측치가 포함되면 제외한 결과 출력"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2b8b2982",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Petal_Length</th>\n",
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       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
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       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sepal_Length  Sepal_Width  Petal_Length  Petal_Width\n",
       "0           5.1          NaN           NaN          0.2\n",
       "1           4.9          3.0           1.4          0.2\n",
       "2           4.7          3.2           1.3          0.2\n",
       "3           4.6          3.1           1.5          0.2\n",
       "4           5.0          3.6           1.4          0.2\n",
       "5           5.4          3.9           1.7          0.4\n",
       "6           NaN          NaN           NaN          NaN\n",
       "7           5.0          3.4           NaN          0.2"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_na2.iloc[:, :-1]  # 마지막 열을 제외한 결과"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "04c065a8",
   "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>Sepal_Length</th>\n",
       "      <th>Sepal_Width</th>\n",
       "      <th>Petal_Length</th>\n",
       "      <th>Petal_Width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
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       "      <th>3</th>\n",
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       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sepal_Length  Sepal_Width  Petal_Length  Petal_Width\n",
       "0           5.1          NaN           NaN          0.2\n",
       "1           4.9          3.0           1.4          0.2\n",
       "2           4.7          3.2           1.3          0.2\n",
       "3           4.6          3.1           1.5          0.2\n",
       "4           5.0          3.6           1.4          0.2\n",
       "5           5.4          3.9           1.7          0.4\n",
       "7           5.0          3.4           NaN          0.2"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_na2.iloc[:, :-1].dropna(how = \"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6797db5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.9"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Sepal_Length\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f97f4359",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.3"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Sepal_Length\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3e6a76c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.853846153846154"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Sepal_Length\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ce2b5a79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.8"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Sepal_Length\"].quantile()  # 중위값 q=50 default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a4f15adc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.8"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Sepal_Length\"].median()  # 중위값 q=50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7b0971bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.1"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Sepal_Length\"].quantile(q = 0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "10df7540",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.8"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Sepal_Length\"].quantile(q = 0.50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5cedf196",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.4"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Sepal_Length\"].quantile(q = 0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "17f935d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Q01. 각 수치형 변수의 결측치 개수 총합은?\n",
    "df.iloc[:, :-1].isna().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "6b519db4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.17561945818840946"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Q02. sepal_width 변수의 결측치를 평균으로 대치한 값의 분산은?\n",
    "df2 = df.fillna(value = {\"Sepal_Width\": df[\"Sepal_Width\"].mean()})\n",
    "df2[\"Sepal_Width\"].var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "77ad24e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Q03. 평균을 기준으로 1.5 표준편차를 넘어서는 값을 이상치라고 간주할 때\n",
    "#      Sepal.Length 변수를 기준으로 이상치인 row 개수는 몇 개인가?\n",
    "df3 = pd.read_csv(\"강의자료/실습파일/iris.csv\")\n",
    "sl_mean = df3[\"Sepal.Length\"].mean()\n",
    "sl_std = df3[\"Sepal.Length\"].std()\n",
    "cond_1 = df3[\"Sepal.Length\"] < (sl_mean - 1.5 * sl_std)\n",
    "cond_2 = df3[\"Sepal.Length\"] > (sl_mean + 1.5 * sl_std)\n",
    "df_out = df.loc[(cond_1) | (cond_2), ]   # or : |\n",
    "len(df_out)"
   ]
  }
 ],
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