week10_pandas_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas 다루기 - 2\n",
    "지난 시간에 판다스의 시리즈, 데이터 프레임의 개념과 기본 사용 방법을 알아보았다.  \n",
    "이번 시간에는 판다스의 더 심화된 고급 기능 사용 방법들에 대해서 알아보겠다.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multilevel Indexing\n",
    "\n",
    "먼저 지난 시간에는 2009년 알콜 소비량만 담고 있는 데이터를 사용하였는데,  \n",
    "여기서 확장하여 연도별 알콜 소비량을 csv 파일을 읽어와 데이터 프레임을 만든다.  \n",
    "그리고 인덱스로 State와 Year 두 가지 컬럼을 지정해준다.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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></th>\n",
       "      <th>Beer</th>\n",
       "      <th>Wine</th>\n",
       "      <th>Spirits</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Alabama</th>\n",
       "      <th>1977</th>\n",
       "      <td>0.99</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>0.98</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979</th>\n",
       "      <td>0.98</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>0.96</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Beer  Wine  Spirits\n",
       "State   Year                     \n",
       "Alabama 1977  0.99  0.13     0.84\n",
       "        1978  0.98  0.12     0.88\n",
       "        1979  0.98  0.12     0.84\n",
       "        1980  0.96  0.16     0.74\n",
       "        1981  1.00  0.19     0.73"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('niaaa-report.csv', index_col=['State', 'Year'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "두 컬럼으로 인덱싱이 걸려있으므로, 이 둘을 활용하여 데이터 접근이 가능하다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Beer</th>\n",
       "      <th>Wine</th>\n",
       "      <th>Spirits</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1977</th>\n",
       "      <td>0.99</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>0.98</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979</th>\n",
       "      <td>0.98</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>0.96</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Beer  Wine  Spirits\n",
       "Year                     \n",
       "1977  0.99  0.13     0.84\n",
       "1978  0.98  0.12     0.88\n",
       "1979  0.98  0.12     0.84\n",
       "1980  0.96  0.16     0.74\n",
       "1981  1.00  0.19     0.73"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Alabama'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Beer       1.18\n",
       "Wine       0.17\n",
       "Spirits    0.51\n",
       "Name: (Alabama, 1999), dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[('Alabama', 1999)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Beer</th>\n",
       "      <th>Wine</th>\n",
       "      <th>Spirits</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Alabama</th>\n",
       "      <th>2008</th>\n",
       "      <td>1.24</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>1.20</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Alaska</th>\n",
       "      <th>1977</th>\n",
       "      <td>1.19</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>1.39</td>\n",
       "      <td>0.45</td>\n",
       "      <td>1.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Beer  Wine  Spirits\n",
       "State   Year                     \n",
       "Alabama 2008  1.24  0.22     0.58\n",
       "        2009  1.20  0.22     0.58\n",
       "Alaska  1977  1.19  0.42     1.70\n",
       "        1978  1.39  0.45     1.68"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[('Alabama', 2008):('Alaska', 1978)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 결측치 다루기\n",
    "머신러닝 프로젝트를 진행할 때 가장 먼저 확인하는 것은  \n",
    "데이터의 결측치가 있는지 여부이다.  \n",
    "판다스의 다양한 함수들을 통해 결측치를 확인하고, 이를 적절히 처리할 수 있다.  \n",
    "이를 위해서 먼저 nan 값을 포함한 데이터 프레임을 생성한다.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Beer</th>\n",
       "      <th>Wine</th>\n",
       "      <th>Spirits</th>\n",
       "      <th>Water</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>South Carolina</th>\n",
       "      <td>1.36</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.77</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Dakota</th>\n",
       "      <td>1.53</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.88</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Samoa</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                Beer  Wine  Spirits  Water\n",
       "State                                     \n",
       "South Carolina  1.36  0.24     0.77    NaN\n",
       "South Dakota    1.53  0.22     0.88    NaN\n",
       "Samoa            NaN   NaN      NaN    NaN"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alco2009 = pd.read_csv('niaaa-report2009.csv', index_col='State')\n",
    "s_states = [state for state in alco2009.index if state[0]=='S'] + ['Samoa']\n",
    "drinks = list(alco2009.columns) + ['Water']\n",
    "nan_df = alco2009.reindex(s_states, columns=drinks)\n",
    "nan_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Beer       1\n",
       "Wine       1\n",
       "Spirits    1\n",
       "Water      3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nan_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "결측치가 포함된 열과 행을 그대로 날려버릴 수 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Beer</th>\n",
       "      <th>Wine</th>\n",
       "      <th>Spirits</th>\n",
       "      <th>Water</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Beer, Wine, Spirits, Water]\n",
       "Index: []"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Nan 값이 하나라도 포함되어 있으면 해당 열, 행을 모두 날려버림\n",
    "nan_df.dropna(how='any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Beer</th>\n",
       "      <th>Wine</th>\n",
       "      <th>Spirits</th>\n",
       "      <th>Water</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>South Carolina</th>\n",
       "      <td>1.36</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.77</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Dakota</th>\n",
       "      <td>1.53</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.88</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Beer  Wine  Spirits  Water\n",
       "State                                     \n",
       "South Carolina  1.36  0.24     0.77    NaN\n",
       "South Dakota    1.53  0.22     0.88    NaN"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 모든 행 값, 혹은 열 값이 Nan 이어야 날려버림\n",
    "nan_df.dropna(how='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>Beer</th>\n",
       "      <th>Wine</th>\n",
       "      <th>Spirits</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>South Carolina</th>\n",
       "      <td>1.36</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Dakota</th>\n",
       "      <td>1.53</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Samoa</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Beer  Wine  Spirits\n",
       "State                              \n",
       "South Carolina  1.36  0.24     0.77\n",
       "South Dakota    1.53  0.22     0.88\n",
       "Samoa            NaN   NaN      NaN"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nan_df.dropna(how='all', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "결측값이 있는 자리에 새로운 값을 채워 넣을 수도 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Beer</th>\n",
       "      <th>Wine</th>\n",
       "      <th>Spirits</th>\n",
       "      <th>Water</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>South Carolina</th>\n",
       "      <td>1.36</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Dakota</th>\n",
       "      <td>1.53</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Samoa</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
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      "text/plain": [
       "                Beer  Wine  Spirits  Water\n",
       "State                                     \n",
       "South Carolina  1.36  0.24     0.77    0.0\n",
       "South Dakota    1.53  0.22     0.88    0.0\n",
       "Samoa           0.00  0.00     0.00    0.0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nan_df.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Beer</th>\n",
       "      <th>Wine</th>\n",
       "      <th>Spirits</th>\n",
       "      <th>Water</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>South Carolina</th>\n",
       "      <td>1.36</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.77</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Dakota</th>\n",
       "      <td>1.53</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.88</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Samoa</th>\n",
       "      <td>1.53</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.88</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Beer  Wine  Spirits  Water\n",
       "State                                     \n",
       "South Carolina  1.36  0.24     0.77    NaN\n",
       "South Dakota    1.53  0.22     0.88    NaN\n",
       "Samoa           1.53  0.22     0.88    NaN"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 이전 행 값으로 채워 넣는 기법\n",
    "nan_df.fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Beer</th>\n",
       "      <th>Wine</th>\n",
       "      <th>Spirits</th>\n",
       "      <th>Water</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>South Carolina</th>\n",
       "      <td>1.36</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Dakota</th>\n",
       "      <td>1.53</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Samoa</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Beer  Wine  Spirits  Water\n",
       "State                                     \n",
       "South Carolina  1.36  0.24     0.77   0.77\n",
       "South Dakota    1.53  0.22     0.88   0.88\n",
       "Samoa            NaN   NaN      NaN    NaN"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 이전 열 값으로 채워 넣는 기법\n",
    "nan_df.fillna(method='ffill', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 마치며\n",
    "\n",
    "이번 시간에는 머신 러닝 프로젝트 진행 시에 필요한 \n",
    "판다스의 고급 기능들에 대해서 알아보았다.  \n",
    "다음 시간에는 마지막으로 판다스를 활용하여 데이터의 다양한 통계 지표들을  \n",
    "추출하는 방법을 알아보도록 하겠다."
   ]
  }
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