Pandas 101.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas 101 (with SQL)\n",
    "이 자료는 데잇걸즈 3기 수업자료로 만들어졌고, [Pandas 10분 완성](https://dataitgirls2.github.io/10minutes2pandas/)을 참고했습니다.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TOC\n",
    "1. Object Creation (객체 생성)\n",
    "2. Viewing Data (데이터 확인하기)\n",
    "3. Selection (선택)\n",
    "4. Missing Data (결측치)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Object Creation (객체 생성)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = pd.Series([1, 2, 3, 4, 5, np.nan, 8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    2.0\n",
       "2    3.0\n",
       "3    4.0\n",
       "4    5.0\n",
       "5    NaN\n",
       "6    8.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Series s의 길이\n",
    "len(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'A': 1, \n",
    "                   'B': 'String',\n",
    "                   'C': s,\n",
    "                   'D': pd.Series([2, 3, 4, 5, 6, 7, 8])})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   A       B    C  D\n",
       "0  1  String  1.0  2\n",
       "1  1  String  2.0  3\n",
       "2  1  String  3.0  4\n",
       "3  1  String  4.0  5\n",
       "4  1  String  5.0  6\n",
       "5  1  String  NaN  7\n",
       "6  1  String  8.0  8"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DataFrame df의 길이\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A      int64\n",
       "B     object\n",
       "C    float64\n",
       "D      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read data from outside"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "house_price = pd.read_csv('https://raw.githubusercontent.com/dataitgirls3/Data/master/house_price_sample.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1460"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(house_price)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Viewing Data (데이터 확인하기)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터의 일부를 눈으로 확인하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
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       "      <th>D</th>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6</td>\n",
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       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   A       B    C  D\n",
       "2  1  String  3.0  4\n",
       "3  1  String  4.0  5\n",
       "4  1  String  5.0  6\n",
       "5  1  String  NaN  7\n",
       "6  1  String  8.0  8"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df.head()\n",
    "# df.head(2)\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# house_price.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 데이터 프레임의 컬럼만 가져오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['A', 'B', 'C', 'D'], dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# house_price.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 데이터 프레임의 값만 가져오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 'String', 1.0, 2],\n",
       "       [1, 'String', 2.0, 3],\n",
       "       [1, 'String', 3.0, 4],\n",
       "       [1, 'String', 4.0, 5],\n",
       "       [1, 'String', 5.0, 6],\n",
       "       [1, 'String', nan, 7],\n",
       "       [1, 'String', 8.0, 8]], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 간단 요약통계"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.833333</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2.483277</td>\n",
       "      <td>2.160247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>3.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>6.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "         A         C         D\n",
       "count  7.0  6.000000  7.000000\n",
       "mean   1.0  3.833333  5.000000\n",
       "std    0.0  2.483277  2.160247\n",
       "min    1.0  1.000000  2.000000\n",
       "25%    1.0  2.250000  3.500000\n",
       "50%    1.0  3.500000  5.000000\n",
       "75%    1.0  4.750000  6.500000\n",
       "max    1.0  8.000000  8.000000"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# house_price.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 데이터 프레임 전치"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "        0       1       2       3       4       5       6\n",
       "A       1       1       1       1       1       1       1\n",
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       "C       1       2       3       4       5     NaN       8\n",
       "D       2       3       4       5       6       7       8"
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     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 데이터 정렬하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A       B    C  D\n",
       "6  1  String  8.0  8\n",
       "5  1  String  NaN  7\n",
       "4  1  String  5.0  6\n",
       "3  1  String  4.0  5\n",
       "2  1  String  3.0  4\n",
       "1  1  String  2.0  3\n",
       "0  1  String  1.0  2"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(axis=0, ascending=False)\n",
    "# df.sort_index(axis=1, ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A       B    C  D\n",
       "6  1  String  8.0  8\n",
       "5  1  String  NaN  7\n",
       "4  1  String  5.0  6\n",
       "3  1  String  4.0  5\n",
       "2  1  String  3.0  4\n",
       "1  1  String  2.0  3\n",
       "0  1  String  1.0  2"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''sql\n",
    "SELECT *\n",
    "FROM df\n",
    "ORDER BY df.D DESC\n",
    "'''\n",
    "\n",
    "df.sort_values(by='D', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# house_price.sort_values(by='SalePrice', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Selection (선택)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    2.0\n",
       "2    3.0\n",
       "3    4.0\n",
       "4    5.0\n",
       "5    NaN\n",
       "6    8.0\n",
       "Name: C, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''sql\n",
    "SELECT df.C\n",
    "FROM df\n",
    "'''\n",
    "\n",
    "df['C']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selection by Label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selectino by Label하고 헷갈릴 수 있으니까 index를 날짜로 바꿔주겠습니다.\n",
    "# Pandas에서 날짜는 이렇게 만들 수 있습니다.\n",
    "\n",
    "df.index = pd.date_range('20190801', periods=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dates안의 첫번째 데이터: 2019-08-01 00:00:00\n"
     ]
    }
   ],
   "source": [
    "print('dates안의 첫번째 데이터: ' + str(dates[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A         1\n",
       "B    String\n",
       "C         1\n",
       "D         2\n",
       "Name: 2019-08-01 00:00:00, dtype: object"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "df.loc[index, column]\n",
    "'''\n",
    "\n",
    "# df.loc[dates[0], :]\n",
    "df.loc[dates[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-08-01</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-02</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-03</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            A       B\n",
       "2019-08-01  1  String\n",
       "2019-08-02  1  String\n",
       "2019-08-03  1  String"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['20190801':'20190803', ['A', 'B']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-08-01</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-02</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-03</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-04</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-05</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-06</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-07</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            A       B\n",
       "2019-08-01  1  String\n",
       "2019-08-02  1  String\n",
       "2019-08-03  1  String\n",
       "2019-08-04  1  String\n",
       "2019-08-05  1  String\n",
       "2019-08-06  1  String\n",
       "2019-08-07  1  String"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df.loc[:,'A']\n",
    "df.loc[:,['A', 'B']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "172 µs ± 27.9 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
      "133 µs ± 4.97 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit df.loc['20190801', 'A']\n",
    "%timeit df.at['20190801', 'A']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selection by Position\n",
    "Selection by Label에서 loc, at을 사용했다면, 데이터의 위치를 이용해 selection을 할 때에는 iloc, iat을 사용합니다. 데이터의 위치를 Index라고 하기 때문입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A         1\n",
       "B    String\n",
       "C         2\n",
       "D         3\n",
       "Name: 2019-08-02 00:00:00, dtype: object"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0]\n",
    "df.iloc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-08-01</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-02</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            A       B\n",
       "2019-08-01  1  String\n",
       "2019-08-02  1  String"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:2, 0:2]\n",
    "df.iloc[[0, 1], [0, 1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.27 µs ± 473 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n",
      "5.62 µs ± 98.5 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit df.iloc[0, 0]\n",
    "%timeit df.iat[0, 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boolean Indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Boolean Indexing 공부를 위해서 기존의 데이터프레임에 컬럼을 하나 추가 해보겠습니다.\n",
    "df['F'] = ['Sydney', 'Brisbane', 'Brisbane', 'Melbourne', 'Sydney', 'Sydney', 'Sydney']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2019-08-01     True\n",
       "2019-08-02    False\n",
       "2019-08-03    False\n",
       "2019-08-04    False\n",
       "2019-08-05     True\n",
       "2019-08-06     True\n",
       "2019-08-07     True\n",
       "Freq: D, Name: F, dtype: bool"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''sql\n",
    "SELECT *\n",
    "FROM df\n",
    "WHERE df.F = 'Sydney'\n",
    "'''\n",
    "\n",
    "df['F'] == 'Sydney'\n",
    "# df[df['F'] == 'Sydney']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe thead th {\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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-08-01</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Sydney</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-04</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "      <td>Melbourne</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-05</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6</td>\n",
       "      <td>Sydney</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-06</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>Sydney</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-07</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8</td>\n",
       "      <td>Sydney</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            A       B    C  D          F\n",
       "2019-08-01  1  String  1.0  2     Sydney\n",
       "2019-08-04  1  String  4.0  5  Melbourne\n",
       "2019-08-05  1  String  5.0  6     Sydney\n",
       "2019-08-06  1  String  NaN  7     Sydney\n",
       "2019-08-07  1  String  8.0  8     Sydney"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''sql\n",
    "SELECT *\n",
    "FROM df\n",
    "WEHRE df.F IN ('Sydney', 'Melbourne')\n",
    "'''\n",
    "\n",
    "df[df['F'].isin(['Sydney', 'Melbourne'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-08-01</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Sydney</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-02</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Brisbane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-03</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>Brisbane</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            A       B    C  D         F\n",
       "2019-08-01  1  String  1.0  2    Sydney\n",
       "2019-08-02  1  String  2.0  3  Brisbane\n",
       "2019-08-03  1  String  3.0  4  Brisbane"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''sql\n",
    "SELECT *\n",
    "FROM df\n",
    "WHERE df.D < 5\n",
    "'''\n",
    "\n",
    "df[df['D'] < 5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-08-01</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Sydney</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-02</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Brisbane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-03</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>Brisbane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-04</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "      <td>Melbourne</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-05</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6</td>\n",
       "      <td>Sydney</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-07</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8</td>\n",
       "      <td>Sydney</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            A       B    C  D          F\n",
       "2019-08-01  1  String  1.0  2     Sydney\n",
       "2019-08-02  1  String  2.0  3   Brisbane\n",
       "2019-08-03  1  String  3.0  4   Brisbane\n",
       "2019-08-04  1  String  4.0  5  Melbourne\n",
       "2019-08-05  1  String  5.0  6     Sydney\n",
       "2019-08-07  1  String  8.0  8     Sydney"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-08-01</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Sydney</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-02</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Brisbane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-03</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Brisbane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-04</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Melbourne</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-05</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>Sydney</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-06</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>This was null</td>\n",
       "      <td>7</td>\n",
       "      <td>Sydney</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-07</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>Sydney</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            A       B              C  D          F\n",
       "2019-08-01  1  String              1  2     Sydney\n",
       "2019-08-02  1  String              2  3   Brisbane\n",
       "2019-08-03  1  String              3  4   Brisbane\n",
       "2019-08-04  1  String              4  5  Melbourne\n",
       "2019-08-05  1  String              5  6     Sydney\n",
       "2019-08-06  1  String  This was null  7     Sydney\n",
       "2019-08-07  1  String              8  8     Sydney"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna('This was null')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-08-01</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Sydney</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-02</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Brisbane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-03</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>Brisbane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-04</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5</td>\n",
       "      <td>Melbourne</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-05</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6</td>\n",
       "      <td>Sydney</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-06</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>Sydney</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-08-07</th>\n",
       "      <td>1</td>\n",
       "      <td>String</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8</td>\n",
       "      <td>Sydney</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            A       B    C  D          F\n",
       "2019-08-01  1  String  1.0  2     Sydney\n",
       "2019-08-02  1  String  2.0  3   Brisbane\n",
       "2019-08-03  1  String  3.0  4   Brisbane\n",
       "2019-08-04  1  String  4.0  5  Melbourne\n",
       "2019-08-05  1  String  5.0  6     Sydney\n",
       "2019-08-06  1  String  NaN  7     Sydney\n",
       "2019-08-07  1  String  8.0  8     Sydney"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 복습은\n",
    "[Pandas 10분 완성](https://dataitgirls2.github.io/10minutes2pandas/)으로 해주세요."
   ]
  }
 ],
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