tools_pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**도구 - 판다스(pandas)**\n",
    "\n",
    "*`pandas` 라이브러리는 사용하기 쉬운 고성능 데이터 구조와 데이터 분석 도구를 제공합니다. 주 데이터 구조는 `DataFrame`입니다. 이를 인-메모리(in-memory) 2D 테이블로 생각할 수 있습니다(열 이름과 행 레이블이 있는 스프레드시트와 비슷합니다). 엑셀에 있는 많은 기능을 프로그램에서 사용할 수 있습니다. 여기에는 피봇 테이블이나 다른 열을 기반으로 열을 계산하고 그래프 출력하는 기능 등이 포함됩니다. 열 값으로 행을 그룹핑할 수도 있습니다. 또한 SQL과 비슷하게 테이블을 조인할 수 있습니다. 판다스는 시계열 데이터를 다루는데도 뛰어납니다.*\n",
    "\n",
    "필요 라이브러리:\n",
    "\n",
    "* 넘파이(NumPy) – 넘파이에 익숙하지 않다면 지금 [넘파이 튜토리얼](tools_numpy.ipynb)을 둘러 보세요."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/rickiepark/handson-ml2/blob/master/tools_pandas.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />구글 코랩에서 실행하기</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 설정"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "먼저 `pandas`를 임포트합니다. 보통 `pd`로 임포트합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:24.221923Z",
     "iopub.status.busy": "2020-10-16T07:31:24.220070Z",
     "iopub.status.idle": "2020-10-16T07:31:24.555259Z",
     "shell.execute_reply": "2020-10-16T07:31:24.556630Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `Series` 객체\n",
    "\n",
    "`pandas` 라이브러리는 다음과 같은 유용한 데이터 구조를 포함하고 있습니다:\n",
    "\n",
    "* `Series` 객체를 곧 이어서 설명하겠습니다. `Series` 객체는 1D 배열입니다. (열 이름과 행 레이블을 가진) 스프레드시트의 열과 비슷합니다.\n",
    "* `DataFrame` 객체는 2D 테이블입니다. (열 이름과 행 레이블을 가진) 스프레드시트와 비슷합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `Series` 만들기\n",
    "\n",
    "첫 번째 `Series` 객체를 만들어 보죠!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:24.570090Z",
     "iopub.status.busy": "2020-10-16T07:31:24.568601Z",
     "iopub.status.idle": "2020-10-16T07:31:24.577334Z",
     "shell.execute_reply": "2020-10-16T07:31:24.578463Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2\n",
       "1   -1\n",
       "2    3\n",
       "3    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([2,-1,3,5])\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1D `ndarray`와 비슷합니다\n",
    "\n",
    "`Series` 객체는 넘파이 `ndarray`와 비슷하게 동작합니다. 넘파이 함수에 매개변수로 종종 전달할 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:24.588613Z",
     "iopub.status.busy": "2020-10-16T07:31:24.587550Z",
     "iopub.status.idle": "2020-10-16T07:31:24.592275Z",
     "shell.execute_reply": "2020-10-16T07:31:24.593060Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      7.389056\n",
       "1      0.367879\n",
       "2     20.085537\n",
       "3    148.413159\n",
       "dtype: float64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.exp(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Series` 객체에 대한 산술 연산도 가능합니다. `ndarray`와 비슷하게 *원소별*로 적용됩니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:24.600732Z",
     "iopub.status.busy": "2020-10-16T07:31:24.599483Z",
     "iopub.status.idle": "2020-10-16T07:31:24.613392Z",
     "shell.execute_reply": "2020-10-16T07:31:24.614382Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1002\n",
       "1    1999\n",
       "2    3003\n",
       "3    4005\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s + [1000,2000,3000,4000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "넘파이와 비슷하게 `Series`에 하나의 숫자를 더하면 `Series`에 있는 모든 원소에 더해집니다. 이를 *브로드캐스팅*(broadcasting)이라고 합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:24.623100Z",
     "iopub.status.busy": "2020-10-16T07:31:24.621859Z",
     "iopub.status.idle": "2020-10-16T07:31:24.626335Z",
     "shell.execute_reply": "2020-10-16T07:31:24.627258Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1002\n",
       "1     999\n",
       "2    1003\n",
       "3    1005\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s + 1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`*`나 `/` 같은 모든 이항 연산과 심지어 조건 연산에서도 마찬가지입니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:24.636724Z",
     "iopub.status.busy": "2020-10-16T07:31:24.635629Z",
     "iopub.status.idle": "2020-10-16T07:31:24.640020Z",
     "shell.execute_reply": "2020-10-16T07:31:24.641002Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "2    False\n",
       "3    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s < 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 인덱스 레이블\n",
    "\n",
    "`Series` 객체에 있는 각 원소는 *인덱스 레이블*(index label)이라 불리는 고유한 식별자를 가지고 있습니다. 기본적으로 `Series`에 있는 원소의 순서입니다(`0`에서 시작합니다). 하지만 수동으로 인덱스 레이블을 지정할 수도 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:24.652237Z",
     "iopub.status.busy": "2020-10-16T07:31:24.651072Z",
     "iopub.status.idle": "2020-10-16T07:31:24.655568Z",
     "shell.execute_reply": "2020-10-16T07:31:24.656530Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "alice       68\n",
       "bob         83\n",
       "charles    112\n",
       "darwin      68\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2 = pd.Series([68, 83, 112, 68], index=[\"alice\", \"bob\", \"charles\", \"darwin\"])\n",
    "s2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "그다음 `dict`처럼 `Series`를 사용할 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:24.664537Z",
     "iopub.status.busy": "2020-10-16T07:31:24.663371Z",
     "iopub.status.idle": "2020-10-16T07:31:24.667750Z",
     "shell.execute_reply": "2020-10-16T07:31:24.668655Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2[\"bob\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "일반 배열처럼 정수 인덱스를 사용하여 계속 원소에 접근할 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:24.676616Z",
     "iopub.status.busy": "2020-10-16T07:31:24.675524Z",
     "iopub.status.idle": "2020-10-16T07:31:24.680902Z",
     "shell.execute_reply": "2020-10-16T07:31:24.679919Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "레이블이나 정수를 사용해 접근할 때 명확하게 하기 위해 레이블은 `loc` 속성을 사용하고 정수는 `iloc` 속성을 사용하는 것이 좋습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:24.688670Z",
     "iopub.status.busy": "2020-10-16T07:31:24.687572Z",
     "iopub.status.idle": "2020-10-16T07:31:24.691843Z",
     "shell.execute_reply": "2020-10-16T07:31:24.692745Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2.loc[\"bob\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:24.700414Z",
     "iopub.status.busy": "2020-10-16T07:31:24.699308Z",
     "iopub.status.idle": "2020-10-16T07:31:24.704534Z",
     "shell.execute_reply": "2020-10-16T07:31:24.703582Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2.iloc[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Series`는 인덱스 레이블을 슬라이싱할 수도 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:24.713637Z",
     "iopub.status.busy": "2020-10-16T07:31:24.712448Z",
     "iopub.status.idle": "2020-10-16T07:31:24.717062Z",
     "shell.execute_reply": "2020-10-16T07:31:24.718032Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bob         83\n",
       "charles    112\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2.iloc[1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "기본 정수 레이블을 사용할 때 예상 외의 결과를 만들 수 있기 때문에 주의해야 합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:24.727693Z",
     "iopub.status.busy": "2020-10-16T07:31:24.726532Z",
     "iopub.status.idle": "2020-10-16T07:31:24.731715Z",
     "shell.execute_reply": "2020-10-16T07:31:24.730767Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1000\n",
       "1    1001\n",
       "2    1002\n",
       "3    1003\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surprise = pd.Series([1000, 1001, 1002, 1003])\n",
    "surprise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:24.743690Z",
     "iopub.status.busy": "2020-10-16T07:31:24.742592Z",
     "iopub.status.idle": "2020-10-16T07:31:24.747552Z",
     "shell.execute_reply": "2020-10-16T07:31:24.748475Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    1002\n",
       "3    1003\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surprise_slice = surprise[2:]\n",
    "surprise_slice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "보세요. 첫 번째 원소의 인덱스 레이블이 `2`입니다. 따라서 슬라이싱 결과에서 인덱스 레이블 `0`인 원소는 없습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:24.756602Z",
     "iopub.status.busy": "2020-10-16T07:31:24.755419Z",
     "iopub.status.idle": "2020-10-16T07:31:24.759801Z",
     "shell.execute_reply": "2020-10-16T07:31:24.760708Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "키 에러: 0\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    surprise_slice[0]\n",
    "except KeyError as e:\n",
    "    print(\"키 에러:\", e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "하지만 `iloc` 속성을 사용해 정수 인덱스로 원소에 접근할 수 있습니다. `Series` 객체를 사용할 때 `loc`와 `iloc`를 사용하는 것이 좋은 이유입니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:24.768724Z",
     "iopub.status.busy": "2020-10-16T07:31:24.767621Z",
     "iopub.status.idle": "2020-10-16T07:31:24.772216Z",
     "shell.execute_reply": "2020-10-16T07:31:24.773102Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1002"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surprise_slice.iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `dict`에서 초기화\n",
    "\n",
    "`dict`에서 `Series` 객체를 만들 수 있습니다. 키는 인덱스 레이블로 사용됩니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:24.787621Z",
     "iopub.status.busy": "2020-10-16T07:31:24.786507Z",
     "iopub.status.idle": "2020-10-16T07:31:24.792357Z",
     "shell.execute_reply": "2020-10-16T07:31:24.791273Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "alice     68\n",
       "bob       83\n",
       "colin     86\n",
       "darwin    68\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights = {\"alice\": 68, \"bob\": 83, \"colin\": 86, \"darwin\": 68}\n",
    "s3 = pd.Series(weights)\n",
    "s3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Series`에 포함할 원소를 제어하고 `index`를 지정하여 명시적으로 순서를 결정할 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:24.802869Z",
     "iopub.status.busy": "2020-10-16T07:31:24.801619Z",
     "iopub.status.idle": "2020-10-16T07:31:24.805950Z",
     "shell.execute_reply": "2020-10-16T07:31:24.806812Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "colin    86\n",
       "alice    68\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s4 = pd.Series(weights, index = [\"colin\", \"alice\"])\n",
    "s4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 자동 정렬\n",
    "\n",
    "여러 개의 `Series` 객체를 다룰 때 `pandas`는 자동으로 인덱스 레이블에 따라 원소를 정렬합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:24.819432Z",
     "iopub.status.busy": "2020-10-16T07:31:24.818333Z",
     "iopub.status.idle": "2020-10-16T07:31:24.824399Z",
     "shell.execute_reply": "2020-10-16T07:31:24.825448Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['alice', 'bob', 'charles', 'darwin'], dtype='object')\n",
      "Index(['alice', 'bob', 'colin', 'darwin'], dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "alice      136.0\n",
       "bob        166.0\n",
       "charles      NaN\n",
       "colin        NaN\n",
       "darwin     136.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(s2.keys())\n",
    "print(s3.keys())\n",
    "\n",
    "s2 + s3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "만들어진 `Series`는 `s2`와 `s3`의 인덱스 레이블의 합집합을 담고 있습니다. `s2`에 `\"colin\"`이 없고 `s3`에 `\"charles\"`가 없기 때문에 이 원소는 `NaN` 값을 가집니다(Not-a-Number는 *누락*이란 의미입니다).\n",
    "\n",
    "자동 정렬은 구조가 다고 누락된 값이 있는 여러 데이터를 다룰 때 매우 편리합니다. 하지만 올바른 인덱스 레이블을 지정하는 것을 잊는다면 원치않는 결과를 얻을 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:24.839789Z",
     "iopub.status.busy": "2020-10-16T07:31:24.838536Z",
     "iopub.status.idle": "2020-10-16T07:31:24.845810Z",
     "shell.execute_reply": "2020-10-16T07:31:24.846735Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s2 = [ 68  83 112  68]\n",
      "s5 = [1000 1000 1000 1000]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "alice     NaN\n",
       "bob       NaN\n",
       "charles   NaN\n",
       "darwin    NaN\n",
       "0         NaN\n",
       "1         NaN\n",
       "2         NaN\n",
       "3         NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s5 = pd.Series([1000,1000,1000,1000])\n",
    "print(\"s2 =\", s2.values)\n",
    "print(\"s5 =\", s5.values)\n",
    "\n",
    "s2 + s5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "레이블이 하나도 맞지 않기 때문에 판다스가 이 `Series`를 정렬할 수 없습니다. 따라서 모두 `NaN`이 되었습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 스칼라로 초기화\n",
    "\n",
    "스칼라와 인덱스 레이블의 리스트로 `Series` 객체를 초기화할 수도 있습니다: 모든 원소가 이 스칼라 값으로 설정됩니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:24.857808Z",
     "iopub.status.busy": "2020-10-16T07:31:24.856592Z",
     "iopub.status.idle": "2020-10-16T07:31:24.861265Z",
     "shell.execute_reply": "2020-10-16T07:31:24.862233Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "life          42\n",
       "universe      42\n",
       "everything    42\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meaning = pd.Series(42, [\"life\", \"universe\", \"everything\"])\n",
    "meaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `Series` 이름\n",
    "\n",
    "`Series`는 `name`을 가질 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:24.874388Z",
     "iopub.status.busy": "2020-10-16T07:31:24.873208Z",
     "iopub.status.idle": "2020-10-16T07:31:24.878224Z",
     "shell.execute_reply": "2020-10-16T07:31:24.879152Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bob      83\n",
       "alice    68\n",
       "Name: weights, dtype: int64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s6 = pd.Series([83, 68], index=[\"bob\", \"alice\"], name=\"weights\")\n",
    "s6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `Series` 그래프 출력\n",
    "\n",
    "맷플롯립을 사용해 `Series` 데이터를 쉽게 그래프로 출력할 수 있습니다(맷플롯립에 대한 자세한 설명은 [맷플롯립 튜토리얼](tools_matplotlib.ipynb)을 참고하세요). 맷플롯립을 임포트하고 `plot()` 메서드를 호출하면 끝입니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:24.893980Z",
     "iopub.status.busy": "2020-10-16T07:31:24.892680Z",
     "iopub.status.idle": "2020-10-16T07:31:25.181629Z",
     "shell.execute_reply": "2020-10-16T07:31:25.183074Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "temperatures = [4.4,5.1,6.1,6.2,6.1,6.1,5.7,5.2,4.7,4.1,3.9,3.5]\n",
    "s7 = pd.Series(temperatures, name=\"Temperature\")\n",
    "s7.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "데이터를 그래프로 출력하는데 많은 *옵션*이 있습니다. 여기에서 모두 나열할 필요는 없습니다. 특정 종류의 그래프(히스토그램, 파이 차트 등)가 필요하면 판다스 문서의 [시각화](http://pandas.pydata.org/pandas-docs/stable/visualization.html) 섹션에서 예제 코드를 참고하세요."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 시간 다루기\n",
    "\n",
    "많은 데이터셋에 타임스탬프가 포함되어 있습니다. 판다스는 이런 데이터를 다루는데 뛰어납니다:\n",
    "* (2016Q3 같은) 기간과 (\"monthly\" 같은) 빈도를 표현할 수 있습니다.\n",
    "* 기간을 실제 타임스탬프로 변환하거나 그 반대로 변환할 수 있습니다.\n",
    "* 데이터를 리샘플링하고 원하는 방식으로 값을 모을 수 있습니다.\n",
    "* 시간대를 다룰 수 있습니다.\n",
    "\n",
    "## 시간 범위\n",
    "\n",
    "먼저 `pd.date_range()`를 사용해 시계열을 만들어 보죠. 이 함수는 2016년 10월 29일 5:30pm에서 시작하여 12시간마다 하나의 datetime을 담고 있는 `DatetimeIndex`를 반환합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:25.201288Z",
     "iopub.status.busy": "2020-10-16T07:31:25.199933Z",
     "iopub.status.idle": "2020-10-16T07:31:25.204600Z",
     "shell.execute_reply": "2020-10-16T07:31:25.205726Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2016-10-29 17:30:00', '2016-10-29 18:30:00',\n",
       "               '2016-10-29 19:30:00', '2016-10-29 20:30:00',\n",
       "               '2016-10-29 21:30:00', '2016-10-29 22:30:00',\n",
       "               '2016-10-29 23:30:00', '2016-10-30 00:30:00',\n",
       "               '2016-10-30 01:30:00', '2016-10-30 02:30:00',\n",
       "               '2016-10-30 03:30:00', '2016-10-30 04:30:00'],\n",
       "              dtype='datetime64[ns]', freq='H')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range('2016/10/29 5:30pm', periods=12, freq='H')\n",
    "dates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 `DatetimeIndex`를 `Series`의 인덱스로 사용할수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:25.217308Z",
     "iopub.status.busy": "2020-10-16T07:31:25.215973Z",
     "iopub.status.idle": "2020-10-16T07:31:25.220460Z",
     "shell.execute_reply": "2020-10-16T07:31:25.221384Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-10-29 17:30:00    4.4\n",
       "2016-10-29 18:30:00    5.1\n",
       "2016-10-29 19:30:00    6.1\n",
       "2016-10-29 20:30:00    6.2\n",
       "2016-10-29 21:30:00    6.1\n",
       "2016-10-29 22:30:00    6.1\n",
       "2016-10-29 23:30:00    5.7\n",
       "2016-10-30 00:30:00    5.2\n",
       "2016-10-30 01:30:00    4.7\n",
       "2016-10-30 02:30:00    4.1\n",
       "2016-10-30 03:30:00    3.9\n",
       "2016-10-30 04:30:00    3.5\n",
       "Freq: H, dtype: float64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_series = pd.Series(temperatures, dates)\n",
    "temp_series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 시리즈를 그래프로 출력해 보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:25.250697Z",
     "iopub.status.busy": "2020-10-16T07:31:25.249514Z",
     "iopub.status.idle": "2020-10-16T07:31:25.416215Z",
     "shell.execute_reply": "2020-10-16T07:31:25.417660Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_series.plot(kind=\"bar\")\n",
    "\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 리샘플링\n",
    "\n",
    "판다스는 매우 간단하게 시계열을 리샘플링할 수 있습니다. `resample()` 메서드를 호출하고 새로운 주기를 지정하면 됩니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:25.425130Z",
     "iopub.status.busy": "2020-10-16T07:31:25.424093Z",
     "iopub.status.idle": "2020-10-16T07:31:25.441445Z",
     "shell.execute_reply": "2020-10-16T07:31:25.440279Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.resample.DatetimeIndexResampler object at 0x7fccdeb8cf28>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_series_freq_2H = temp_series.resample(\"2H\")\n",
    "temp_series_freq_2H"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "리샘플링 연산은 사실 지연된 연산입니다. 그래서 `Series` 객체 대신 `DatetimeIndexResampler` 객체가 반환됩니다. 실제 리샘플링 연산을 수행하려면 `mean()` 같은 메서드를 호출할 수 있습니다. 이 메서드는 연속적인 시간 쌍에 대해 평균을 계산합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:25.448657Z",
     "iopub.status.busy": "2020-10-16T07:31:25.446880Z",
     "iopub.status.idle": "2020-10-16T07:31:25.451798Z",
     "shell.execute_reply": "2020-10-16T07:31:25.452607Z"
    }
   },
   "outputs": [],
   "source": [
    "temp_series_freq_2H = temp_series_freq_2H.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "결과를 그래프로 출력해 보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:25.510003Z",
     "iopub.status.busy": "2020-10-16T07:31:25.508970Z",
     "iopub.status.idle": "2020-10-16T07:31:25.597932Z",
     "shell.execute_reply": "2020-10-16T07:31:25.599362Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_series_freq_2H.plot(kind=\"bar\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2시간 간격으로 어떻게 값이 수집되었는지 확인해 보세요. 예를 들어 6-8pm 간격을 보면 6:30pm에서 `5.1`이고 7:30pm에서 `6.1`입니다. 리샘플링 후에 `5.1`과 `6.1`의 평균인 `5.6` 하나를 얻었습니다. 평균말고 어떤 집계 함수(aggregation function)도 사용할 수 있습니다. 예를 들어 각 기간에서 최솟값을 찾을 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:25.607951Z",
     "iopub.status.busy": "2020-10-16T07:31:25.606867Z",
     "iopub.status.idle": "2020-10-16T07:31:25.611285Z",
     "shell.execute_reply": "2020-10-16T07:31:25.612183Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-10-29 16:00:00    4.4\n",
       "2016-10-29 18:00:00    5.1\n",
       "2016-10-29 20:00:00    6.1\n",
       "2016-10-29 22:00:00    5.7\n",
       "2016-10-30 00:00:00    4.7\n",
       "2016-10-30 02:00:00    3.9\n",
       "2016-10-30 04:00:00    3.5\n",
       "Freq: 2H, dtype: float64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_series_freq_2H = temp_series.resample(\"2H\").min()\n",
    "temp_series_freq_2H"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "또는 동일한 효과를 내는 `apply()` 메서드를 사용할 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:25.629068Z",
     "iopub.status.busy": "2020-10-16T07:31:25.627935Z",
     "iopub.status.idle": "2020-10-16T07:31:25.632613Z",
     "shell.execute_reply": "2020-10-16T07:31:25.633842Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-10-29 16:00:00    4.4\n",
       "2016-10-29 18:00:00    5.1\n",
       "2016-10-29 20:00:00    6.1\n",
       "2016-10-29 22:00:00    5.7\n",
       "2016-10-30 00:00:00    4.7\n",
       "2016-10-30 02:00:00    3.9\n",
       "2016-10-30 04:00:00    3.5\n",
       "Freq: 2H, dtype: float64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_series_freq_2H = temp_series.resample(\"2H\").apply(np.min)\n",
    "temp_series_freq_2H"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 업샘플링과 보간\n",
    "\n",
    "다운샘플링의 예를 보았습니다. 하지만 업샘플링(즉, 빈도를 높입니다)도 할 수 있습니다. 하지만 데이터에 구멍을 만듭니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:25.646456Z",
     "iopub.status.busy": "2020-10-16T07:31:25.645255Z",
     "iopub.status.idle": "2020-10-16T07:31:25.649342Z",
     "shell.execute_reply": "2020-10-16T07:31:25.650294Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-10-29 17:30:00    4.4\n",
       "2016-10-29 17:45:00    NaN\n",
       "2016-10-29 18:00:00    NaN\n",
       "2016-10-29 18:15:00    NaN\n",
       "2016-10-29 18:30:00    5.1\n",
       "2016-10-29 18:45:00    NaN\n",
       "2016-10-29 19:00:00    NaN\n",
       "2016-10-29 19:15:00    NaN\n",
       "2016-10-29 19:30:00    6.1\n",
       "2016-10-29 19:45:00    NaN\n",
       "Freq: 15T, dtype: float64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_series_freq_15min = temp_series.resample(\"15Min\").mean()\n",
    "temp_series_freq_15min.head(n=10) # `head`는 상위 n 개의 값만 출력합니다"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "한가지 방법은 보간으로 사이를 채우는 것입니다. 이렇게 하려면 `interpolate()` 메서드를 호출합니다. 기본값은 선형 보간이지만 3차 보간(cubic interpolation) 같은 다른 방법을 선택할 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:25.661000Z",
     "iopub.status.busy": "2020-10-16T07:31:25.659794Z",
     "iopub.status.idle": "2020-10-16T07:31:25.736426Z",
     "shell.execute_reply": "2020-10-16T07:31:25.735488Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-10-29 17:30:00    4.400000\n",
       "2016-10-29 17:45:00    4.452911\n",
       "2016-10-29 18:00:00    4.605113\n",
       "2016-10-29 18:15:00    4.829758\n",
       "2016-10-29 18:30:00    5.100000\n",
       "2016-10-29 18:45:00    5.388992\n",
       "2016-10-29 19:00:00    5.669887\n",
       "2016-10-29 19:15:00    5.915839\n",
       "2016-10-29 19:30:00    6.100000\n",
       "2016-10-29 19:45:00    6.203621\n",
       "Freq: 15T, dtype: float64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_series_freq_15min = temp_series.resample(\"15Min\").interpolate(method=\"cubic\")\n",
    "temp_series_freq_15min.head(n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:25.766770Z",
     "iopub.status.busy": "2020-10-16T07:31:25.764902Z",
     "iopub.status.idle": "2020-10-16T07:31:25.929920Z",
     "shell.execute_reply": "2020-10-16T07:31:25.931211Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_series.plot(label=\"Period: 1 hour\")\n",
    "temp_series_freq_15min.plot(label=\"Period: 15 minutes\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 시간대\n",
    "\n",
    "기본적으로 datetime은 *단순*합니다. 시간대(timezone)을 고려하지 않죠. 따라서 2016-10-30 02:30는 파리나 뉴욕이나 2016년 10월 30일 2:30pm입니다. `tz_localize()` 메서드로 시간대를 고려한 datetime을 만들 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:25.939079Z",
     "iopub.status.busy": "2020-10-16T07:31:25.937520Z",
     "iopub.status.idle": "2020-10-16T07:31:25.969458Z",
     "shell.execute_reply": "2020-10-16T07:31:25.970302Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-10-29 17:30:00-04:00    4.4\n",
       "2016-10-29 18:30:00-04:00    5.1\n",
       "2016-10-29 19:30:00-04:00    6.1\n",
       "2016-10-29 20:30:00-04:00    6.2\n",
       "2016-10-29 21:30:00-04:00    6.1\n",
       "2016-10-29 22:30:00-04:00    6.1\n",
       "2016-10-29 23:30:00-04:00    5.7\n",
       "2016-10-30 00:30:00-04:00    5.2\n",
       "2016-10-30 01:30:00-04:00    4.7\n",
       "2016-10-30 02:30:00-04:00    4.1\n",
       "2016-10-30 03:30:00-04:00    3.9\n",
       "2016-10-30 04:30:00-04:00    3.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_series_ny = temp_series.tz_localize(\"America/New_York\")\n",
    "temp_series_ny"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "모든 datetime에 `-04:00`이 추가됩니다. 즉 모든 시간은 [UTC](https://en.wikipedia.org/wiki/Coordinated_Universal_Time) - 4시간을 의미합니다.\n",
    "\n",
    "다음처럼 파리 시간대로 바꿀 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:25.975427Z",
     "iopub.status.busy": "2020-10-16T07:31:25.974415Z",
     "iopub.status.idle": "2020-10-16T07:31:25.981708Z",
     "shell.execute_reply": "2020-10-16T07:31:25.982510Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-10-29 23:30:00+02:00    4.4\n",
       "2016-10-30 00:30:00+02:00    5.1\n",
       "2016-10-30 01:30:00+02:00    6.1\n",
       "2016-10-30 02:30:00+02:00    6.2\n",
       "2016-10-30 02:30:00+01:00    6.1\n",
       "2016-10-30 03:30:00+01:00    6.1\n",
       "2016-10-30 04:30:00+01:00    5.7\n",
       "2016-10-30 05:30:00+01:00    5.2\n",
       "2016-10-30 06:30:00+01:00    4.7\n",
       "2016-10-30 07:30:00+01:00    4.1\n",
       "2016-10-30 08:30:00+01:00    3.9\n",
       "2016-10-30 09:30:00+01:00    3.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_series_paris = temp_series_ny.tz_convert(\"Europe/Paris\")\n",
    "temp_series_paris"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "UTC와의 차이가 `+02:00`에서 `+01:00`으로 바뀐 것을 알 수 있습니다. 이는 프랑스가 10월 30일 3am에 겨울 시간으로 바꾸기 때문입니다(2am으로 바뀝니다). 따라서 2:30am이 두 번 등장합니다! 시간대가 없는 표현으로 돌아가 보죠(시간대가 없이 지역 시간으로 매시간 로그를 기록하는 경우 이와 비슷할 것입니다):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:25.992461Z",
     "iopub.status.busy": "2020-10-16T07:31:25.991439Z",
     "iopub.status.idle": "2020-10-16T07:31:25.996320Z",
     "shell.execute_reply": "2020-10-16T07:31:25.997330Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-10-29 23:30:00    4.4\n",
       "2016-10-30 00:30:00    5.1\n",
       "2016-10-30 01:30:00    6.1\n",
       "2016-10-30 02:30:00    6.2\n",
       "2016-10-30 02:30:00    6.1\n",
       "2016-10-30 03:30:00    6.1\n",
       "2016-10-30 04:30:00    5.7\n",
       "2016-10-30 05:30:00    5.2\n",
       "2016-10-30 06:30:00    4.7\n",
       "2016-10-30 07:30:00    4.1\n",
       "2016-10-30 08:30:00    3.9\n",
       "2016-10-30 09:30:00    3.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_series_paris_naive = temp_series_paris.tz_localize(None)\n",
    "temp_series_paris_naive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이렇게 되면 `02:30`이 정말 애매합니다. 시간대가 없는 datetime을 파리 시간대로 바꿀 때 에러가 발생합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.006045Z",
     "iopub.status.busy": "2020-10-16T07:31:26.004859Z",
     "iopub.status.idle": "2020-10-16T07:31:26.009183Z",
     "shell.execute_reply": "2020-10-16T07:31:26.010136Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pytz.exceptions.AmbiguousTimeError'>\n",
      "Cannot infer dst time from 2016-10-30 02:30:00, try using the 'ambiguous' argument\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    temp_series_paris_naive.tz_localize(\"Europe/Paris\")\n",
    "except Exception as e:\n",
    "    print(type(e))\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "다행히 `ambiguous` 매개변수를 사용하면 판다스가 타임스탬프의 순서를 기반으로 적절한 DST(일광 절약 시간제)를 추측합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.021302Z",
     "iopub.status.busy": "2020-10-16T07:31:26.020109Z",
     "iopub.status.idle": "2020-10-16T07:31:26.024328Z",
     "shell.execute_reply": "2020-10-16T07:31:26.025304Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-10-29 23:30:00+02:00    4.4\n",
       "2016-10-30 00:30:00+02:00    5.1\n",
       "2016-10-30 01:30:00+02:00    6.1\n",
       "2016-10-30 02:30:00+02:00    6.2\n",
       "2016-10-30 02:30:00+01:00    6.1\n",
       "2016-10-30 03:30:00+01:00    6.1\n",
       "2016-10-30 04:30:00+01:00    5.7\n",
       "2016-10-30 05:30:00+01:00    5.2\n",
       "2016-10-30 06:30:00+01:00    4.7\n",
       "2016-10-30 07:30:00+01:00    4.1\n",
       "2016-10-30 08:30:00+01:00    3.9\n",
       "2016-10-30 09:30:00+01:00    3.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_series_paris_naive.tz_localize(\"Europe/Paris\", ambiguous=\"infer\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 기간\n",
    "\n",
    "`pd.period_range()` 함수는 `DatetimeIndex`가 아니라 `PeriodIndex`를 반환합니다. 예를 들어 2016과 2017년의 전체 분기를 가져와 보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.038037Z",
     "iopub.status.busy": "2020-10-16T07:31:26.036774Z",
     "iopub.status.idle": "2020-10-16T07:31:26.043054Z",
     "shell.execute_reply": "2020-10-16T07:31:26.044094Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2016Q1', '2016Q2', '2016Q3', '2016Q4', '2017Q1', '2017Q2',\n",
       "             '2017Q3', '2017Q4'],\n",
       "            dtype='period[Q-DEC]', freq='Q-DEC')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quarters = pd.period_range('2016Q1', periods=8, freq='Q')\n",
    "quarters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`PeriodIndex`에 숫자 `N`을 추가하면 `PeriodIndex` 빈도의 `N` 배만큼 이동시킵니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.052480Z",
     "iopub.status.busy": "2020-10-16T07:31:26.051248Z",
     "iopub.status.idle": "2020-10-16T07:31:26.055609Z",
     "shell.execute_reply": "2020-10-16T07:31:26.056455Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2016Q4', '2017Q1', '2017Q2', '2017Q3', '2017Q4', '2018Q1',\n",
       "             '2018Q2', '2018Q3'],\n",
       "            dtype='period[Q-DEC]', freq='Q-DEC')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quarters + 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`asfreq()` 메서드를 사용하면 `PeriodIndex`의 빈도를 바꿀 수 있습니다. 모든 기간이 늘어나거나 줄어듭니다. 예를 들어 분기 기간을 모두 월별 기간으로 바꾸어 보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.065095Z",
     "iopub.status.busy": "2020-10-16T07:31:26.064006Z",
     "iopub.status.idle": "2020-10-16T07:31:26.068838Z",
     "shell.execute_reply": "2020-10-16T07:31:26.069767Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2016-03', '2016-06', '2016-09', '2016-12', '2017-03', '2017-06',\n",
       "             '2017-09', '2017-12'],\n",
       "            dtype='period[M]', freq='M')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quarters.asfreq(\"M\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "기본적으로 `asfreq`는 각 기간의 끝에 맞춥니다. 기간의 시작에 맞추도록 변경할 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.079083Z",
     "iopub.status.busy": "2020-10-16T07:31:26.077918Z",
     "iopub.status.idle": "2020-10-16T07:31:26.082533Z",
     "shell.execute_reply": "2020-10-16T07:31:26.083439Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2016-01', '2016-04', '2016-07', '2016-10', '2017-01', '2017-04',\n",
       "             '2017-07', '2017-10'],\n",
       "            dtype='period[M]', freq='M')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quarters.asfreq(\"M\", how=\"start\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "간격을 늘릴 수도 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.092258Z",
     "iopub.status.busy": "2020-10-16T07:31:26.091161Z",
     "iopub.status.idle": "2020-10-16T07:31:26.096928Z",
     "shell.execute_reply": "2020-10-16T07:31:26.095946Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2016', '2016', '2016', '2016', '2017', '2017', '2017', '2017'], dtype='period[A-DEC]', freq='A-DEC')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quarters.asfreq(\"A\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "물론 `PeriodIndex`로 `Series`를 만들 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.107793Z",
     "iopub.status.busy": "2020-10-16T07:31:26.106610Z",
     "iopub.status.idle": "2020-10-16T07:31:26.112111Z",
     "shell.execute_reply": "2020-10-16T07:31:26.111095Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016Q1    300\n",
       "2016Q2    320\n",
       "2016Q3    290\n",
       "2016Q4    390\n",
       "2017Q1    320\n",
       "2017Q2    360\n",
       "2017Q3    310\n",
       "2017Q4    410\n",
       "Freq: Q-DEC, dtype: int64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quarterly_revenue = pd.Series([300, 320, 290, 390, 320, 360, 310, 410], index = quarters)\n",
    "quarterly_revenue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.140793Z",
     "iopub.status.busy": "2020-10-16T07:31:26.133036Z",
     "iopub.status.idle": "2020-10-16T07:31:26.258810Z",
     "shell.execute_reply": "2020-10-16T07:31:26.260006Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "quarterly_revenue.plot(kind=\"line\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`to_timestamp`를 호출해서 기간을 타임스탬프로 변경할 수 있습니다. 기본적으로 기간의 첫 번째 날을 반환합니다. 하지만 `how`와 `freq`를 지정해서 기간의 마지막 시간을 얻을 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.268964Z",
     "iopub.status.busy": "2020-10-16T07:31:26.267481Z",
     "iopub.status.idle": "2020-10-16T07:31:26.271761Z",
     "shell.execute_reply": "2020-10-16T07:31:26.272657Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016-03-31 23:59:59.999999999    300\n",
       "2016-06-30 23:59:59.999999999    320\n",
       "2016-09-30 23:59:59.999999999    290\n",
       "2016-12-31 23:59:59.999999999    390\n",
       "2017-03-31 23:59:59.999999999    320\n",
       "2017-06-30 23:59:59.999999999    360\n",
       "2017-09-30 23:59:59.999999999    310\n",
       "2017-12-31 23:59:59.999999999    410\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "last_hours = quarterly_revenue.to_timestamp(how=\"end\", freq=\"H\")\n",
    "last_hours"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`to_peroid`를 호출하면 다시 기간으로 돌아갑니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.283017Z",
     "iopub.status.busy": "2020-10-16T07:31:26.281932Z",
     "iopub.status.idle": "2020-10-16T07:31:26.286503Z",
     "shell.execute_reply": "2020-10-16T07:31:26.287371Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016Q1    300\n",
       "2016Q2    320\n",
       "2016Q3    290\n",
       "2016Q4    390\n",
       "2017Q1    320\n",
       "2017Q2    360\n",
       "2017Q3    310\n",
       "2017Q4    410\n",
       "Freq: Q-DEC, dtype: int64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "last_hours.to_period()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "판다스는 여러 가지 시간 관련 함수를 많이 제공합니다. [온라인 문서](http://pandas.pydata.org/pandas-docs/stable/timeseries.html)를 확인해 보세요. 예를 하나 들면 2016년 매월 마지막 업무일의 9시를 얻는 방법은 다음과 같습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.302110Z",
     "iopub.status.busy": "2020-10-16T07:31:26.300761Z",
     "iopub.status.idle": "2020-10-16T07:31:26.305420Z",
     "shell.execute_reply": "2020-10-16T07:31:26.306523Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2016-01-29 09:00', '2016-02-29 09:00', '2016-03-31 09:00',\n",
       "             '2016-04-29 09:00', '2016-05-31 09:00', '2016-06-30 09:00',\n",
       "             '2016-07-29 09:00', '2016-08-31 09:00', '2016-09-30 09:00',\n",
       "             '2016-10-31 09:00', '2016-11-30 09:00', '2016-12-30 09:00'],\n",
       "            dtype='period[H]', freq='H')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "months_2016 = pd.period_range(\"2016\", periods=12, freq=\"M\")\n",
    "one_day_after_last_days = months_2016.asfreq(\"D\") + 1\n",
    "last_bdays = one_day_after_last_days.to_timestamp() - pd.tseries.offsets.BDay()\n",
    "last_bdays.to_period(\"H\") + 9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `DataFrame` 객체\n",
    "\n",
    "데이터프레임 객체는 스프레드시트를 표현합니다. 셀 값, 열 이름, 행 인덱스 레이블을 가집니다. 다른 열을 바탕으로 열을 계산하는 식을 쓸 수 있고 피봇 테이블을 만들고, 행을 그룹핑하고, 그래프를 그릴 수 있습니다. `DataFrame`을 `Series`의 딕셔너리로 볼 수 있습니다.\n",
    "\n",
    "## `DataFrame` 만들기\n",
    "\n",
    "`Series` 객체의 딕셔너리를 전달하여 데이터프레임을 만들 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.323422Z",
     "iopub.status.busy": "2020-10-16T07:31:26.322071Z",
     "iopub.status.idle": "2020-10-16T07:31:26.329974Z",
     "shell.execute_reply": "2020-10-16T07:31:26.330949Z"
    }
   },
   "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>weight</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>children</th>\n",
       "      <th>hobby</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>68</td>\n",
       "      <td>1985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Biking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>83</td>\n",
       "      <td>1984</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Dancing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>112</td>\n",
       "      <td>1992</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         weight  birthyear  children    hobby\n",
       "alice        68       1985       NaN   Biking\n",
       "bob          83       1984       3.0  Dancing\n",
       "charles     112       1992       0.0      NaN"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people_dict = {\n",
    "    \"weight\": pd.Series([68, 83, 112], index=[\"alice\", \"bob\", \"charles\"]),\n",
    "    \"birthyear\": pd.Series([1984, 1985, 1992], index=[\"bob\", \"alice\", \"charles\"], name=\"year\"),\n",
    "    \"children\": pd.Series([0, 3], index=[\"charles\", \"bob\"]),\n",
    "    \"hobby\": pd.Series([\"Biking\", \"Dancing\"], index=[\"alice\", \"bob\"]),\n",
    "}\n",
    "people = pd.DataFrame(people_dict)\n",
    "people"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "몇가지 알아 두어야 할 것은 다음과 같습니다:\n",
    "\n",
    "* `Series`는 인덱스를 기반으로 자동으로 정렬됩니다.\n",
    "* 누란된 값은 `NaN`으로 표현됩니다.\n",
    "* `Series` 이름은 무시됩니다(`\"year\"`란 이름은 삭제됩니다).\n",
    "* `DataFrame`은 주피터 노트북에서 멋지게 출력됩니다!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "예상하는 방식으로 열을 참조할 수 있고 `Serires` 객체가 반환됩니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.337478Z",
     "iopub.status.busy": "2020-10-16T07:31:26.336279Z",
     "iopub.status.idle": "2020-10-16T07:31:26.340155Z",
     "shell.execute_reply": "2020-10-16T07:31:26.341062Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "alice      1985\n",
       "bob        1984\n",
       "charles    1992\n",
       "Name: birthyear, dtype: int64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people[\"birthyear\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "동시에 여러 개의 열을 선택할 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.358410Z",
     "iopub.status.busy": "2020-10-16T07:31:26.357116Z",
     "iopub.status.idle": "2020-10-16T07:31:26.362134Z",
     "shell.execute_reply": "2020-10-16T07:31:26.363233Z"
    }
   },
   "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>birthyear</th>\n",
       "      <th>hobby</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>1985</td>\n",
       "      <td>Biking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>1984</td>\n",
       "      <td>Dancing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>1992</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         birthyear    hobby\n",
       "alice         1985   Biking\n",
       "bob           1984  Dancing\n",
       "charles       1992      NaN"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people[[\"birthyear\", \"hobby\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "열 리스트나 행 인덱스 레이블을 `DataFrame` 생성자에 전달하면 해당 열과 행으로 채워진 데이터프레임이 반환됩니다. 예를 들면:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.383281Z",
     "iopub.status.busy": "2020-10-16T07:31:26.381945Z",
     "iopub.status.idle": "2020-10-16T07:31:26.386650Z",
     "shell.execute_reply": "2020-10-16T07:31:26.387670Z"
    }
   },
   "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>birthyear</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>1984.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>1985.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eugene</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        birthyear  weight height\n",
       "bob        1984.0    83.0    NaN\n",
       "alice      1985.0    68.0    NaN\n",
       "eugene        NaN     NaN    NaN"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d2 = pd.DataFrame(\n",
    "        people_dict,\n",
    "        columns=[\"birthyear\", \"weight\", \"height\"],\n",
    "        index=[\"bob\", \"alice\", \"eugene\"]\n",
    "     )\n",
    "d2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`DataFrame`을 만드는 또 다른 편리한 방법은 `ndarray`나 리스트의 리스트로 모든 값을 생성자에게 전달하고 열 이름과 행 인덱스 레이블을 각기 지정하는 것입니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.398021Z",
     "iopub.status.busy": "2020-10-16T07:31:26.396793Z",
     "iopub.status.idle": "2020-10-16T07:31:26.413413Z",
     "shell.execute_reply": "2020-10-16T07:31:26.414561Z"
    }
   },
   "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>birthyear</th>\n",
       "      <th>children</th>\n",
       "      <th>hobby</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>1985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Biking</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>1984</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Dancing</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>1992</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         birthyear  children    hobby  weight\n",
       "alice         1985       NaN   Biking      68\n",
       "bob           1984       3.0  Dancing      83\n",
       "charles       1992       0.0      NaN     112"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values = [\n",
    "            [1985, np.nan, \"Biking\",   68],\n",
    "            [1984, 3,      \"Dancing\",  83],\n",
    "            [1992, 0,      np.nan,    112]\n",
    "         ]\n",
    "d3 = pd.DataFrame(\n",
    "        values,\n",
    "        columns=[\"birthyear\", \"children\", \"hobby\", \"weight\"],\n",
    "        index=[\"alice\", \"bob\", \"charles\"]\n",
    "     )\n",
    "d3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "누락된 값을 지정하려면 `np.nan`이나 넘파이 마스크 배열을 사용합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.425026Z",
     "iopub.status.busy": "2020-10-16T07:31:26.423521Z",
     "iopub.status.idle": "2020-10-16T07:31:26.438112Z",
     "shell.execute_reply": "2020-10-16T07:31:26.439307Z"
    }
   },
   "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>birthyear</th>\n",
       "      <th>children</th>\n",
       "      <th>hobby</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>1985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Biking</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>1984</td>\n",
       "      <td>3</td>\n",
       "      <td>Dancing</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>1992</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        birthyear children    hobby weight\n",
       "alice        1985      NaN   Biking     68\n",
       "bob          1984        3  Dancing     83\n",
       "charles      1992        0      NaN    112"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "masked_array = np.ma.asarray(values, dtype=np.object)\n",
    "masked_array[(0, 2), (1, 2)] = np.ma.masked\n",
    "d3 = pd.DataFrame(\n",
    "        masked_array,\n",
    "        columns=[\"birthyear\", \"children\", \"hobby\", \"weight\"],\n",
    "        index=[\"alice\", \"bob\", \"charles\"]\n",
    "     )\n",
    "d3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`ndarray` 대신에 `DataFrame` 객체를 전달할 수도 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.450844Z",
     "iopub.status.busy": "2020-10-16T07:31:26.449596Z",
     "iopub.status.idle": "2020-10-16T07:31:26.454051Z",
     "shell.execute_reply": "2020-10-16T07:31:26.455073Z"
    }
   },
   "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>hobby</th>\n",
       "      <th>children</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>Biking</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>Dancing</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         hobby children\n",
       "alice   Biking      NaN\n",
       "bob    Dancing        3"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d4 = pd.DataFrame(\n",
    "         d3,\n",
    "         columns=[\"hobby\", \"children\"],\n",
    "         index=[\"alice\", \"bob\"]\n",
    "     )\n",
    "d4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "딕셔너리의 딕셔너리(또는 리스트의 리스트)로 `DataFrame`을 만들 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.472738Z",
     "iopub.status.busy": "2020-10-16T07:31:26.466378Z",
     "iopub.status.idle": "2020-10-16T07:31:26.476045Z",
     "shell.execute_reply": "2020-10-16T07:31:26.477041Z"
    }
   },
   "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>birthyear</th>\n",
       "      <th>hobby</th>\n",
       "      <th>weight</th>\n",
       "      <th>children</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>1985</td>\n",
       "      <td>Biking</td>\n",
       "      <td>68</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>1984</td>\n",
       "      <td>Dancing</td>\n",
       "      <td>83</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>1992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         birthyear    hobby  weight  children\n",
       "alice         1985   Biking      68       NaN\n",
       "bob           1984  Dancing      83       3.0\n",
       "charles       1992      NaN     112       0.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people = pd.DataFrame({\n",
    "    \"birthyear\": {\"alice\":1985, \"bob\": 1984, \"charles\": 1992},\n",
    "    \"hobby\": {\"alice\":\"Biking\", \"bob\": \"Dancing\"},\n",
    "    \"weight\": {\"alice\":68, \"bob\": 83, \"charles\": 112},\n",
    "    \"children\": {\"bob\": 3, \"charles\": 0}\n",
    "})\n",
    "people"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 멀티 인덱싱\n",
    "\n",
    "모든 열이 같은 크기의 튜플이면 멀티 인덱스로 인식합니다. 열 인덱스 레이블에도 같은 방식이 적용됩니다. 예를 들면:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.504047Z",
     "iopub.status.busy": "2020-10-16T07:31:26.502935Z",
     "iopub.status.idle": "2020-10-16T07:31:26.507572Z",
     "shell.execute_reply": "2020-10-16T07:31:26.508472Z"
    }
   },
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">public</th>\n",
       "      <th colspan=\"2\" halign=\"left\">private</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>birthyear</th>\n",
       "      <th>hobby</th>\n",
       "      <th>weight</th>\n",
       "      <th>children</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Paris</th>\n",
       "      <th>alice</th>\n",
       "      <td>1985</td>\n",
       "      <td>Biking</td>\n",
       "      <td>68</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>1984</td>\n",
       "      <td>Dancing</td>\n",
       "      <td>83</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>London</th>\n",
       "      <th>charles</th>\n",
       "      <td>1992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  public          private         \n",
       "               birthyear    hobby  weight children\n",
       "Paris  alice        1985   Biking      68      NaN\n",
       "       bob          1984  Dancing      83      3.0\n",
       "London charles      1992      NaN     112      0.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d5 = pd.DataFrame(\n",
    "  {\n",
    "    (\"public\", \"birthyear\"):\n",
    "        {(\"Paris\",\"alice\"):1985, (\"Paris\",\"bob\"): 1984, (\"London\",\"charles\"): 1992},\n",
    "    (\"public\", \"hobby\"):\n",
    "        {(\"Paris\",\"alice\"):\"Biking\", (\"Paris\",\"bob\"): \"Dancing\"},\n",
    "    (\"private\", \"weight\"):\n",
    "        {(\"Paris\",\"alice\"):68, (\"Paris\",\"bob\"): 83, (\"London\",\"charles\"): 112},\n",
    "    (\"private\", \"children\"):\n",
    "        {(\"Paris\", \"alice\"):np.nan, (\"Paris\",\"bob\"): 3, (\"London\",\"charles\"): 0}\n",
    "  }\n",
    ")\n",
    "d5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이제 `\"public\"` 열을 모두 담은 `DataFrame`을 손쉽게 만들 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.523804Z",
     "iopub.status.busy": "2020-10-16T07:31:26.522714Z",
     "iopub.status.idle": "2020-10-16T07:31:26.527772Z",
     "shell.execute_reply": "2020-10-16T07:31:26.528758Z"
    }
   },
   "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></th>\n",
       "      <th>birthyear</th>\n",
       "      <th>hobby</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Paris</th>\n",
       "      <th>alice</th>\n",
       "      <td>1985</td>\n",
       "      <td>Biking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>1984</td>\n",
       "      <td>Dancing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>London</th>\n",
       "      <th>charles</th>\n",
       "      <td>1992</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                birthyear    hobby\n",
       "Paris  alice         1985   Biking\n",
       "       bob           1984  Dancing\n",
       "London charles       1992      NaN"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d5[\"public\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.539921Z",
     "iopub.status.busy": "2020-10-16T07:31:26.538672Z",
     "iopub.status.idle": "2020-10-16T07:31:26.543468Z",
     "shell.execute_reply": "2020-10-16T07:31:26.544424Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Paris   alice       Biking\n",
       "        bob        Dancing\n",
       "London  charles        NaN\n",
       "Name: (public, hobby), dtype: object"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d5[\"public\", \"hobby\"]  # d5[\"public\"][\"hobby\"]와 같습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 레벨 낮추기\n",
    "\n",
    "`d5`를 다시 확인해 보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.562331Z",
     "iopub.status.busy": "2020-10-16T07:31:26.560989Z",
     "iopub.status.idle": "2020-10-16T07:31:26.565827Z",
     "shell.execute_reply": "2020-10-16T07:31:26.566935Z"
    }
   },
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">public</th>\n",
       "      <th colspan=\"2\" halign=\"left\">private</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>birthyear</th>\n",
       "      <th>hobby</th>\n",
       "      <th>weight</th>\n",
       "      <th>children</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Paris</th>\n",
       "      <th>alice</th>\n",
       "      <td>1985</td>\n",
       "      <td>Biking</td>\n",
       "      <td>68</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>1984</td>\n",
       "      <td>Dancing</td>\n",
       "      <td>83</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>London</th>\n",
       "      <th>charles</th>\n",
       "      <td>1992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  public          private         \n",
       "               birthyear    hobby  weight children\n",
       "Paris  alice        1985   Biking      68      NaN\n",
       "       bob          1984  Dancing      83      3.0\n",
       "London charles      1992      NaN     112      0.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "열의 레벨(level)이 2개이고 인덱스 레벨이 2개입니다. `droplevel()`을 사용해 열 레벨을 낮출 수 있습니다(인덱스도 마찬가지입니다):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.584582Z",
     "iopub.status.busy": "2020-10-16T07:31:26.583237Z",
     "iopub.status.idle": "2020-10-16T07:31:26.587765Z",
     "shell.execute_reply": "2020-10-16T07:31:26.588935Z"
    }
   },
   "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></th>\n",
       "      <th>birthyear</th>\n",
       "      <th>hobby</th>\n",
       "      <th>weight</th>\n",
       "      <th>children</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Paris</th>\n",
       "      <th>alice</th>\n",
       "      <td>1985</td>\n",
       "      <td>Biking</td>\n",
       "      <td>68</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>1984</td>\n",
       "      <td>Dancing</td>\n",
       "      <td>83</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>London</th>\n",
       "      <th>charles</th>\n",
       "      <td>1992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                birthyear    hobby  weight  children\n",
       "Paris  alice         1985   Biking      68       NaN\n",
       "       bob           1984  Dancing      83       3.0\n",
       "London charles       1992      NaN     112       0.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d5.columns = d5.columns.droplevel(level = 0)\n",
    "d5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 전치\n",
    "\n",
    "`T` 속성을 사용해 열과 인덱스를 바꿀 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.608084Z",
     "iopub.status.busy": "2020-10-16T07:31:26.606912Z",
     "iopub.status.idle": "2020-10-16T07:31:26.611322Z",
     "shell.execute_reply": "2020-10-16T07:31:26.612358Z"
    }
   },
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Paris</th>\n",
       "      <th>London</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>alice</th>\n",
       "      <th>bob</th>\n",
       "      <th>charles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>birthyear</th>\n",
       "      <td>1985</td>\n",
       "      <td>1984</td>\n",
       "      <td>1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hobby</th>\n",
       "      <td>Biking</td>\n",
       "      <td>Dancing</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weight</th>\n",
       "      <td>68</td>\n",
       "      <td>83</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>children</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Paris           London\n",
       "            alice      bob charles\n",
       "birthyear    1985     1984    1992\n",
       "hobby      Biking  Dancing     NaN\n",
       "weight         68       83     112\n",
       "children      NaN        3       0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d6 = d5.T\n",
    "d6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 레벨 스택과 언스택\n",
    "\n",
    "`stack()` 메서드는 가장 낮은 열 레벨을 가장 낮은 인덱스 뒤에 추가합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.620480Z",
     "iopub.status.busy": "2020-10-16T07:31:26.619233Z",
     "iopub.status.idle": "2020-10-16T07:31:26.645121Z",
     "shell.execute_reply": "2020-10-16T07:31:26.645952Z"
    }
   },
   "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></th>\n",
       "      <th>London</th>\n",
       "      <th>Paris</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">birthyear</th>\n",
       "      <th>alice</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>1992</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">hobby</th>\n",
       "      <th>alice</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Biking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Dancing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">weight</th>\n",
       "      <th>alice</th>\n",
       "      <td>NaN</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>NaN</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>112</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">children</th>\n",
       "      <th>bob</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  London    Paris\n",
       "birthyear alice      NaN     1985\n",
       "          bob        NaN     1984\n",
       "          charles   1992      NaN\n",
       "hobby     alice      NaN   Biking\n",
       "          bob        NaN  Dancing\n",
       "weight    alice      NaN       68\n",
       "          bob        NaN       83\n",
       "          charles    112      NaN\n",
       "children  bob        NaN        3\n",
       "          charles      0      NaN"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d7 = d6.stack()\n",
    "d7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`NaN` 값이 생겼습니다. 이전에 없던 조합이 생겼기 때문입니다(예를 들어 `London`에 `bob`이 없었습니다).\n",
    "\n",
    "`unstack()`을 호출하면 반대가 됩니다. 여기에서도 많은 `NaN` 값이 생성됩니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.670564Z",
     "iopub.status.busy": "2020-10-16T07:31:26.669099Z",
     "iopub.status.idle": "2020-10-16T07:31:26.674498Z",
     "shell.execute_reply": "2020-10-16T07:31:26.675704Z"
    }
   },
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">London</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Paris</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>alice</th>\n",
       "      <th>bob</th>\n",
       "      <th>charles</th>\n",
       "      <th>alice</th>\n",
       "      <th>bob</th>\n",
       "      <th>charles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>birthyear</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1992</td>\n",
       "      <td>1985</td>\n",
       "      <td>1984</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>children</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hobby</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Biking</td>\n",
       "      <td>Dancing</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weight</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112</td>\n",
       "      <td>68</td>\n",
       "      <td>83</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          London                Paris                 \n",
       "           alice  bob charles   alice      bob charles\n",
       "birthyear    NaN  NaN    1992    1985     1984     NaN\n",
       "children     NaN  NaN       0     NaN        3     NaN\n",
       "hobby        NaN  NaN     NaN  Biking  Dancing     NaN\n",
       "weight       NaN  NaN     112      68       83     NaN"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d8 = d7.unstack()\n",
    "d8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`unstack`을 다시 호출하면 `Series` 객체가 만들어 집니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.690137Z",
     "iopub.status.busy": "2020-10-16T07:31:26.688874Z",
     "iopub.status.idle": "2020-10-16T07:31:26.693084Z",
     "shell.execute_reply": "2020-10-16T07:31:26.694056Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "London  alice    birthyear        NaN\n",
       "                 children         NaN\n",
       "                 hobby            NaN\n",
       "                 weight           NaN\n",
       "        bob      birthyear        NaN\n",
       "                 children         NaN\n",
       "                 hobby            NaN\n",
       "                 weight           NaN\n",
       "        charles  birthyear       1992\n",
       "                 children           0\n",
       "                 hobby            NaN\n",
       "                 weight           112\n",
       "Paris   alice    birthyear       1985\n",
       "                 children         NaN\n",
       "                 hobby         Biking\n",
       "                 weight            68\n",
       "        bob      birthyear       1984\n",
       "                 children           3\n",
       "                 hobby        Dancing\n",
       "                 weight            83\n",
       "        charles  birthyear        NaN\n",
       "                 children         NaN\n",
       "                 hobby            NaN\n",
       "                 weight           NaN\n",
       "dtype: object"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d9 = d8.unstack()\n",
    "d9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`stack()`과 `unstack()` 메서드를 사용할 때 스택/언스택할 `level`을 선택할 수 있습니다. 심지어 한 번에 여러 개의 레벨을 스택/언스택할 수도 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.719311Z",
     "iopub.status.busy": "2020-10-16T07:31:26.718139Z",
     "iopub.status.idle": "2020-10-16T07:31:26.723311Z",
     "shell.execute_reply": "2020-10-16T07:31:26.724247Z"
    },
    "scrolled": true
   },
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">London</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Paris</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>alice</th>\n",
       "      <th>bob</th>\n",
       "      <th>charles</th>\n",
       "      <th>alice</th>\n",
       "      <th>bob</th>\n",
       "      <th>charles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>birthyear</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1992</td>\n",
       "      <td>1985</td>\n",
       "      <td>1984</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>children</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hobby</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Biking</td>\n",
       "      <td>Dancing</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weight</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112</td>\n",
       "      <td>68</td>\n",
       "      <td>83</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          London                Paris                 \n",
       "           alice  bob charles   alice      bob charles\n",
       "birthyear    NaN  NaN    1992    1985     1984     NaN\n",
       "children     NaN  NaN       0     NaN        3     NaN\n",
       "hobby        NaN  NaN     NaN  Biking  Dancing     NaN\n",
       "weight       NaN  NaN     112      68       83     NaN"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d10 = d9.unstack(level = (0,1))\n",
    "d10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 대부분의 메서드는 수정된 복사본을 반환합니다\n",
    "\n",
    "눈치챘겠지만 `stack()`과 `unstack()` 메서드는 객체를 수정하지 않습니다. 대신 복사본을 만들어 반환합니다. 판다스에 있는 대부분의 메서드들이 이렇게 동작합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 행 참조하기\n",
    "\n",
    "`people` `DataFrame`으로 돌아가 보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.740196Z",
     "iopub.status.busy": "2020-10-16T07:31:26.739030Z",
     "iopub.status.idle": "2020-10-16T07:31:26.743455Z",
     "shell.execute_reply": "2020-10-16T07:31:26.744486Z"
    }
   },
   "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>birthyear</th>\n",
       "      <th>hobby</th>\n",
       "      <th>weight</th>\n",
       "      <th>children</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>1985</td>\n",
       "      <td>Biking</td>\n",
       "      <td>68</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>1984</td>\n",
       "      <td>Dancing</td>\n",
       "      <td>83</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>1992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         birthyear    hobby  weight  children\n",
       "alice         1985   Biking      68       NaN\n",
       "bob           1984  Dancing      83       3.0\n",
       "charles       1992      NaN     112       0.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`loc` 속성으로 열 대신 행을 참조할 수 있습니다. `DataFrame`의 열 이름이 행 인덱스 레이블로 매핑된 `Series` 객체가 반환됩니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.753868Z",
     "iopub.status.busy": "2020-10-16T07:31:26.752625Z",
     "iopub.status.idle": "2020-10-16T07:31:26.756975Z",
     "shell.execute_reply": "2020-10-16T07:31:26.757905Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "birthyear    1992\n",
       "hobby         NaN\n",
       "weight        112\n",
       "children        0\n",
       "Name: charles, dtype: object"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people.loc[\"charles\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`iloc` 속성을 사용해 정수 인덱스로 행을 참조할 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.767760Z",
     "iopub.status.busy": "2020-10-16T07:31:26.766655Z",
     "iopub.status.idle": "2020-10-16T07:31:26.771119Z",
     "shell.execute_reply": "2020-10-16T07:31:26.772031Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "birthyear    1992\n",
       "hobby         NaN\n",
       "weight        112\n",
       "children        0\n",
       "Name: charles, dtype: object"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people.iloc[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "행을 슬라이싱할 수 있으며 `DataFrame` 객체가 반환됩니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.786461Z",
     "iopub.status.busy": "2020-10-16T07:31:26.785104Z",
     "iopub.status.idle": "2020-10-16T07:31:26.789947Z",
     "shell.execute_reply": "2020-10-16T07:31:26.791052Z"
    }
   },
   "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",
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       "    }\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>birthyear</th>\n",
       "      <th>hobby</th>\n",
       "      <th>weight</th>\n",
       "      <th>children</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>1984</td>\n",
       "      <td>Dancing</td>\n",
       "      <td>83</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>1992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         birthyear    hobby  weight  children\n",
       "bob           1984  Dancing      83       3.0\n",
       "charles       1992      NaN     112       0.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people.iloc[1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "마자믹으로 불리언 배열을 전달하여 해당하는 행을 가져올 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.808646Z",
     "iopub.status.busy": "2020-10-16T07:31:26.807301Z",
     "iopub.status.idle": "2020-10-16T07:31:26.811968Z",
     "shell.execute_reply": "2020-10-16T07:31:26.813013Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>birthyear</th>\n",
       "      <th>hobby</th>\n",
       "      <th>weight</th>\n",
       "      <th>children</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>1985</td>\n",
       "      <td>Biking</td>\n",
       "      <td>68</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>1992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         birthyear   hobby  weight  children\n",
       "alice         1985  Biking      68       NaN\n",
       "charles       1992     NaN     112       0.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people[np.array([True, False, True])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "불리언 표현식을 사용할 때 아주 유용합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.829832Z",
     "iopub.status.busy": "2020-10-16T07:31:26.828532Z",
     "iopub.status.idle": "2020-10-16T07:31:26.833273Z",
     "shell.execute_reply": "2020-10-16T07:31:26.834311Z"
    }
   },
   "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>birthyear</th>\n",
       "      <th>hobby</th>\n",
       "      <th>weight</th>\n",
       "      <th>children</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>1985</td>\n",
       "      <td>Biking</td>\n",
       "      <td>68</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>1984</td>\n",
       "      <td>Dancing</td>\n",
       "      <td>83</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       birthyear    hobby  weight  children\n",
       "alice       1985   Biking      68       NaN\n",
       "bob         1984  Dancing      83       3.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people[people[\"birthyear\"] < 1990]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 열 추가, 삭제\n",
    "\n",
    "`DataFrame`을 `Series`의 딕셔너리처럼 다룰 수 있습니다. 따라서 다음 같이 쓸 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.848712Z",
     "iopub.status.busy": "2020-10-16T07:31:26.847466Z",
     "iopub.status.idle": "2020-10-16T07:31:26.852951Z",
     "shell.execute_reply": "2020-10-16T07:31:26.851924Z"
    }
   },
   "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>birthyear</th>\n",
       "      <th>hobby</th>\n",
       "      <th>weight</th>\n",
       "      <th>children</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>1985</td>\n",
       "      <td>Biking</td>\n",
       "      <td>68</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>1984</td>\n",
       "      <td>Dancing</td>\n",
       "      <td>83</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>1992</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         birthyear    hobby  weight  children\n",
       "alice         1985   Biking      68       NaN\n",
       "bob           1984  Dancing      83       3.0\n",
       "charles       1992      NaN     112       0.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.873305Z",
     "iopub.status.busy": "2020-10-16T07:31:26.872037Z",
     "iopub.status.idle": "2020-10-16T07:31:26.876795Z",
     "shell.execute_reply": "2020-10-16T07:31:26.877922Z"
    }
   },
   "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>hobby</th>\n",
       "      <th>weight</th>\n",
       "      <th>age</th>\n",
       "      <th>over 30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>Biking</td>\n",
       "      <td>68</td>\n",
       "      <td>33</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>Dancing</td>\n",
       "      <td>83</td>\n",
       "      <td>34</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>NaN</td>\n",
       "      <td>112</td>\n",
       "      <td>26</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           hobby  weight  age  over 30\n",
       "alice     Biking      68   33     True\n",
       "bob      Dancing      83   34     True\n",
       "charles      NaN     112   26    False"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people[\"age\"] = 2018 - people[\"birthyear\"]  # \"age\" 열을 추가합니다\n",
    "people[\"over 30\"] = people[\"age\"] > 30      # \"over 30\" 열을 추가합니다\n",
    "birthyears = people.pop(\"birthyear\")\n",
    "del people[\"children\"]\n",
    "\n",
    "people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.886529Z",
     "iopub.status.busy": "2020-10-16T07:31:26.885421Z",
     "iopub.status.idle": "2020-10-16T07:31:26.890520Z",
     "shell.execute_reply": "2020-10-16T07:31:26.889567Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "alice      1985\n",
       "bob        1984\n",
       "charles    1992\n",
       "Name: birthyear, dtype: int64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "birthyears"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "새로운 열을 추가할 때 행의 개수는 같아야 합니다. 누락된 행은 `NaN`으로 채워지고 추가적인 행은 무시됩니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.910981Z",
     "iopub.status.busy": "2020-10-16T07:31:26.909610Z",
     "iopub.status.idle": "2020-10-16T07:31:26.914677Z",
     "shell.execute_reply": "2020-10-16T07:31:26.915783Z"
    }
   },
   "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>hobby</th>\n",
       "      <th>weight</th>\n",
       "      <th>age</th>\n",
       "      <th>over 30</th>\n",
       "      <th>pets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>Biking</td>\n",
       "      <td>68</td>\n",
       "      <td>33</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>Dancing</td>\n",
       "      <td>83</td>\n",
       "      <td>34</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>NaN</td>\n",
       "      <td>112</td>\n",
       "      <td>26</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           hobby  weight  age  over 30  pets\n",
       "alice     Biking      68   33     True   NaN\n",
       "bob      Dancing      83   34     True   0.0\n",
       "charles      NaN     112   26    False   5.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people[\"pets\"] = pd.Series({\"bob\": 0, \"charles\": 5, \"eugene\":1})  # alice 누락됨, eugene은 무시됨\n",
    "people"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "새로운 열을 추가할 때 기본적으로 (오른쪽) 끝에 추가됩니다. `insert()` 메서드를 사용해 다른 곳에 열을 추가할 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.935076Z",
     "iopub.status.busy": "2020-10-16T07:31:26.933817Z",
     "iopub.status.idle": "2020-10-16T07:31:26.938345Z",
     "shell.execute_reply": "2020-10-16T07:31:26.939377Z"
    }
   },
   "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>hobby</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>age</th>\n",
       "      <th>over 30</th>\n",
       "      <th>pets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>Biking</td>\n",
       "      <td>172</td>\n",
       "      <td>68</td>\n",
       "      <td>33</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>Dancing</td>\n",
       "      <td>181</td>\n",
       "      <td>83</td>\n",
       "      <td>34</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>NaN</td>\n",
       "      <td>185</td>\n",
       "      <td>112</td>\n",
       "      <td>26</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           hobby  height  weight  age  over 30  pets\n",
       "alice     Biking     172      68   33     True   NaN\n",
       "bob      Dancing     181      83   34     True   0.0\n",
       "charles      NaN     185     112   26    False   5.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people.insert(1, \"height\", [172, 181, 185])\n",
    "people"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 새로운 열 할당하기\n",
    "\n",
    "`assign()` 메서드를 호출하여 새로운 열을 만들 수도 있습니다. 이는 새로운 `DataFrame` 객체를 반환하며 원본 객체는 변경되지 않습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.963676Z",
     "iopub.status.busy": "2020-10-16T07:31:26.962421Z",
     "iopub.status.idle": "2020-10-16T07:31:26.966980Z",
     "shell.execute_reply": "2020-10-16T07:31:26.968012Z"
    }
   },
   "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>hobby</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>age</th>\n",
       "      <th>over 30</th>\n",
       "      <th>pets</th>\n",
       "      <th>body_mass_index</th>\n",
       "      <th>has_pets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>Biking</td>\n",
       "      <td>172</td>\n",
       "      <td>68</td>\n",
       "      <td>33</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.985398</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>Dancing</td>\n",
       "      <td>181</td>\n",
       "      <td>83</td>\n",
       "      <td>34</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.335002</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>NaN</td>\n",
       "      <td>185</td>\n",
       "      <td>112</td>\n",
       "      <td>26</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "      <td>32.724617</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           hobby  height  weight  age  over 30  pets  body_mass_index  \\\n",
       "alice     Biking     172      68   33     True   NaN        22.985398   \n",
       "bob      Dancing     181      83   34     True   0.0        25.335002   \n",
       "charles      NaN     185     112   26    False   5.0        32.724617   \n",
       "\n",
       "         has_pets  \n",
       "alice       False  \n",
       "bob         False  \n",
       "charles      True  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people.assign(\n",
    "    body_mass_index = people[\"weight\"] / (people[\"height\"] / 100) ** 2,\n",
    "    has_pets = people[\"pets\"] > 0\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "할당문 안에서 만든 열은 접근할 수 없습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:26.979950Z",
     "iopub.status.busy": "2020-10-16T07:31:26.978686Z",
     "iopub.status.idle": "2020-10-16T07:31:26.984376Z",
     "shell.execute_reply": "2020-10-16T07:31:26.983363Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "키 에러: 'body_mass_index'\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    people.assign(\n",
    "        body_mass_index = people[\"weight\"] / (people[\"height\"] / 100) ** 2,\n",
    "        overweight = people[\"body_mass_index\"] > 25\n",
    "    )\n",
    "except KeyError as e:\n",
    "    print(\"키 에러:\", e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "해결책은 두 개의 연속된 할당문으로 나누는 것입니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:27.008299Z",
     "iopub.status.busy": "2020-10-16T07:31:26.995147Z",
     "iopub.status.idle": "2020-10-16T07:31:27.014321Z",
     "shell.execute_reply": "2020-10-16T07:31:27.012947Z"
    }
   },
   "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>hobby</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>age</th>\n",
       "      <th>over 30</th>\n",
       "      <th>pets</th>\n",
       "      <th>body_mass_index</th>\n",
       "      <th>overweight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>Biking</td>\n",
       "      <td>172</td>\n",
       "      <td>68</td>\n",
       "      <td>33</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.985398</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>Dancing</td>\n",
       "      <td>181</td>\n",
       "      <td>83</td>\n",
       "      <td>34</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.335002</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>NaN</td>\n",
       "      <td>185</td>\n",
       "      <td>112</td>\n",
       "      <td>26</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "      <td>32.724617</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           hobby  height  weight  age  over 30  pets  body_mass_index  \\\n",
       "alice     Biking     172      68   33     True   NaN        22.985398   \n",
       "bob      Dancing     181      83   34     True   0.0        25.335002   \n",
       "charles      NaN     185     112   26    False   5.0        32.724617   \n",
       "\n",
       "         overweight  \n",
       "alice         False  \n",
       "bob            True  \n",
       "charles        True  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d6 = people.assign(body_mass_index = people[\"weight\"] / (people[\"height\"] / 100) ** 2)\n",
    "d6.assign(overweight = d6[\"body_mass_index\"] > 25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "임시 변수 `d6`를 만들면 불편합니다. `assign()` 메서드를 연결하고 싶겠지만 `people` 객체가 첫 번째 할당문에서 실제로 수정되지 않기 때문에 작동하지 않습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:27.029304Z",
     "iopub.status.busy": "2020-10-16T07:31:27.027741Z",
     "iopub.status.idle": "2020-10-16T07:31:27.032521Z",
     "shell.execute_reply": "2020-10-16T07:31:27.033510Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "키 에러: 'body_mass_index'\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    (people\n",
    "         .assign(body_mass_index = people[\"weight\"] / (people[\"height\"] / 100) ** 2)\n",
    "         .assign(overweight = people[\"body_mass_index\"] > 25)\n",
    "    )\n",
    "except KeyError as e:\n",
    "    print(\"키 에러:\", e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "하지만 걱정하지 마세요. 간단한 방법이 있습니다. `assign()` 메서드에 함수(전형적으로 `lambda` 함수)를 전달하면 `DataFrame`을 매개변수로 이 함수를 호출할 것입니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:27.057521Z",
     "iopub.status.busy": "2020-10-16T07:31:27.043525Z",
     "iopub.status.idle": "2020-10-16T07:31:27.062274Z",
     "shell.execute_reply": "2020-10-16T07:31:27.063582Z"
    }
   },
   "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>hobby</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>age</th>\n",
       "      <th>over 30</th>\n",
       "      <th>pets</th>\n",
       "      <th>body_mass_index</th>\n",
       "      <th>overweight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>Biking</td>\n",
       "      <td>172</td>\n",
       "      <td>68</td>\n",
       "      <td>33</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.985398</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>Dancing</td>\n",
       "      <td>181</td>\n",
       "      <td>83</td>\n",
       "      <td>34</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.335002</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>NaN</td>\n",
       "      <td>185</td>\n",
       "      <td>112</td>\n",
       "      <td>26</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "      <td>32.724617</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           hobby  height  weight  age  over 30  pets  body_mass_index  \\\n",
       "alice     Biking     172      68   33     True   NaN        22.985398   \n",
       "bob      Dancing     181      83   34     True   0.0        25.335002   \n",
       "charles      NaN     185     112   26    False   5.0        32.724617   \n",
       "\n",
       "         overweight  \n",
       "alice         False  \n",
       "bob            True  \n",
       "charles        True  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(people\n",
    "     .assign(body_mass_index = lambda df: df[\"weight\"] / (df[\"height\"] / 100) ** 2)\n",
    "     .assign(overweight = lambda df: df[\"body_mass_index\"] > 25)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "문제가 해결되었군요!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 표현식 평가\n",
    "\n",
    "판다스가 제공하는 뛰어난 기능 하나는 표현식 평가입니다. 이는 `numexpr` 라이브러리에 의존하기 때문에 설치가 되어 있어야 합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:27.088939Z",
     "iopub.status.busy": "2020-10-16T07:31:27.087317Z",
     "iopub.status.idle": "2020-10-16T07:31:27.092870Z",
     "shell.execute_reply": "2020-10-16T07:31:27.094182Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "alice      False\n",
       "bob         True\n",
       "charles     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people.eval(\"weight / (height/100) ** 2 > 25\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "할당 표현식도 지원됩니다. `inplace=True`로 지정하면 수정된 복사본을 만들지 않고 바로 `DataFrame`을 변경합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:27.121360Z",
     "iopub.status.busy": "2020-10-16T07:31:27.119766Z",
     "iopub.status.idle": "2020-10-16T07:31:27.124873Z",
     "shell.execute_reply": "2020-10-16T07:31:27.125987Z"
    }
   },
   "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>hobby</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>age</th>\n",
       "      <th>over 30</th>\n",
       "      <th>pets</th>\n",
       "      <th>body_mass_index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>Biking</td>\n",
       "      <td>172</td>\n",
       "      <td>68</td>\n",
       "      <td>33</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.985398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>Dancing</td>\n",
       "      <td>181</td>\n",
       "      <td>83</td>\n",
       "      <td>34</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.335002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>NaN</td>\n",
       "      <td>185</td>\n",
       "      <td>112</td>\n",
       "      <td>26</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "      <td>32.724617</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           hobby  height  weight  age  over 30  pets  body_mass_index\n",
       "alice     Biking     172      68   33     True   NaN        22.985398\n",
       "bob      Dancing     181      83   34     True   0.0        25.335002\n",
       "charles      NaN     185     112   26    False   5.0        32.724617"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people.eval(\"body_mass_index = weight / (height/100) ** 2\", inplace=True)\n",
    "people"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`'@'`를 접두어로 사용하여 지역 변수나 전역 변수를 참조할 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:27.151724Z",
     "iopub.status.busy": "2020-10-16T07:31:27.150378Z",
     "iopub.status.idle": "2020-10-16T07:31:27.155345Z",
     "shell.execute_reply": "2020-10-16T07:31:27.156398Z"
    }
   },
   "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>hobby</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>age</th>\n",
       "      <th>over 30</th>\n",
       "      <th>pets</th>\n",
       "      <th>body_mass_index</th>\n",
       "      <th>overweight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>Biking</td>\n",
       "      <td>172</td>\n",
       "      <td>68</td>\n",
       "      <td>33</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.985398</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>Dancing</td>\n",
       "      <td>181</td>\n",
       "      <td>83</td>\n",
       "      <td>34</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.335002</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>NaN</td>\n",
       "      <td>185</td>\n",
       "      <td>112</td>\n",
       "      <td>26</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "      <td>32.724617</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           hobby  height  weight  age  over 30  pets  body_mass_index  \\\n",
       "alice     Biking     172      68   33     True   NaN        22.985398   \n",
       "bob      Dancing     181      83   34     True   0.0        25.335002   \n",
       "charles      NaN     185     112   26    False   5.0        32.724617   \n",
       "\n",
       "         overweight  \n",
       "alice         False  \n",
       "bob           False  \n",
       "charles        True  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "overweight_threshold = 30\n",
    "people.eval(\"overweight = body_mass_index > @overweight_threshold\", inplace=True)\n",
    "people"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `DataFrame` 쿼리하기\n",
    "\n",
    "`query()` 메서드를 사용하면 쿼리 표현식에 기반하여 `DataFrame`을 필터링할 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:27.180483Z",
     "iopub.status.busy": "2020-10-16T07:31:27.168045Z",
     "iopub.status.idle": "2020-10-16T07:31:27.185677Z",
     "shell.execute_reply": "2020-10-16T07:31:27.186843Z"
    }
   },
   "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>hobby</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>age</th>\n",
       "      <th>over 30</th>\n",
       "      <th>pets</th>\n",
       "      <th>body_mass_index</th>\n",
       "      <th>overweight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>Dancing</td>\n",
       "      <td>181</td>\n",
       "      <td>83</td>\n",
       "      <td>34</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.335002</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       hobby  height  weight  age  over 30  pets  body_mass_index  overweight\n",
       "bob  Dancing     181      83   34     True   0.0        25.335002       False"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people.query(\"age > 30 and pets == 0\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `DataFrame` 정렬\n",
    "\n",
    "`sort_index` 메서드를 호출하여 `DataFrame`을 정렬할 수 있습니다. 기본적으로 인덱스 레이블을 기준으로 오름차순으로 행을 정렬합니다. 여기에서는 내림차순으로 정렬해 보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:27.209165Z",
     "iopub.status.busy": "2020-10-16T07:31:27.207658Z",
     "iopub.status.idle": "2020-10-16T07:31:27.212703Z",
     "shell.execute_reply": "2020-10-16T07:31:27.213844Z"
    }
   },
   "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>hobby</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>age</th>\n",
       "      <th>over 30</th>\n",
       "      <th>pets</th>\n",
       "      <th>body_mass_index</th>\n",
       "      <th>overweight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>NaN</td>\n",
       "      <td>185</td>\n",
       "      <td>112</td>\n",
       "      <td>26</td>\n",
       "      <td>False</td>\n",
       "      <td>5.0</td>\n",
       "      <td>32.724617</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>Dancing</td>\n",
       "      <td>181</td>\n",
       "      <td>83</td>\n",
       "      <td>34</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.335002</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>Biking</td>\n",
       "      <td>172</td>\n",
       "      <td>68</td>\n",
       "      <td>33</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.985398</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           hobby  height  weight  age  over 30  pets  body_mass_index  \\\n",
       "charles      NaN     185     112   26    False   5.0        32.724617   \n",
       "bob      Dancing     181      83   34     True   0.0        25.335002   \n",
       "alice     Biking     172      68   33     True   NaN        22.985398   \n",
       "\n",
       "         overweight  \n",
       "charles        True  \n",
       "bob           False  \n",
       "alice         False  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people.sort_index(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`sort_index`는 `DataFrame`의 정렬된 *복사본*을 반환합니다. `people`을 직접 수정하려면 `inplace` 매개변수를 `True`로 지정합니다. 또한 `axis=1`로 지정하여 열 대신 행을 정렬할 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:27.235840Z",
     "iopub.status.busy": "2020-10-16T07:31:27.234491Z",
     "iopub.status.idle": "2020-10-16T07:31:27.239396Z",
     "shell.execute_reply": "2020-10-16T07:31:27.240434Z"
    }
   },
   "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>age</th>\n",
       "      <th>body_mass_index</th>\n",
       "      <th>height</th>\n",
       "      <th>hobby</th>\n",
       "      <th>over 30</th>\n",
       "      <th>overweight</th>\n",
       "      <th>pets</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>33</td>\n",
       "      <td>22.985398</td>\n",
       "      <td>172</td>\n",
       "      <td>Biking</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>34</td>\n",
       "      <td>25.335002</td>\n",
       "      <td>181</td>\n",
       "      <td>Dancing</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>26</td>\n",
       "      <td>32.724617</td>\n",
       "      <td>185</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>5.0</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         age  body_mass_index  height    hobby  over 30  overweight  pets  \\\n",
       "alice     33        22.985398     172   Biking     True       False   NaN   \n",
       "bob       34        25.335002     181  Dancing     True       False   0.0   \n",
       "charles   26        32.724617     185      NaN    False        True   5.0   \n",
       "\n",
       "         weight  \n",
       "alice        68  \n",
       "bob          83  \n",
       "charles     112  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people.sort_index(axis=1, inplace=True)\n",
    "people"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "레이블이 아니라 값을 기준으로 `DataFrame`을 정렬하려면 `sort_values`에 정렬하려는 열을 지정합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:27.261726Z",
     "iopub.status.busy": "2020-10-16T07:31:27.260351Z",
     "iopub.status.idle": "2020-10-16T07:31:27.265293Z",
     "shell.execute_reply": "2020-10-16T07:31:27.266432Z"
    }
   },
   "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>age</th>\n",
       "      <th>body_mass_index</th>\n",
       "      <th>height</th>\n",
       "      <th>hobby</th>\n",
       "      <th>over 30</th>\n",
       "      <th>overweight</th>\n",
       "      <th>pets</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>26</td>\n",
       "      <td>32.724617</td>\n",
       "      <td>185</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>5.0</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>33</td>\n",
       "      <td>22.985398</td>\n",
       "      <td>172</td>\n",
       "      <td>Biking</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>34</td>\n",
       "      <td>25.335002</td>\n",
       "      <td>181</td>\n",
       "      <td>Dancing</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         age  body_mass_index  height    hobby  over 30  overweight  pets  \\\n",
       "charles   26        32.724617     185      NaN    False        True   5.0   \n",
       "alice     33        22.985398     172   Biking     True       False   NaN   \n",
       "bob       34        25.335002     181  Dancing     True       False   0.0   \n",
       "\n",
       "         weight  \n",
       "charles     112  \n",
       "alice        68  \n",
       "bob          83  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people.sort_values(by=\"age\", inplace=True)\n",
    "people"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `DataFrame` 그래프 그리기\n",
    "\n",
    "`Series`와 마찬가지로 판다스는 `DataFrame` 기반으로 멋진 그래프를 손쉽게 그릴 수 있습니다.\n",
    "\n",
    "예를 들어 `plot` 메서드를 호출하여 `DataFrame`의 데이터에서 선 그래프를 쉽게 그릴 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:27.304606Z",
     "iopub.status.busy": "2020-10-16T07:31:27.301491Z",
     "iopub.status.idle": "2020-10-16T07:31:27.517164Z",
     "shell.execute_reply": "2020-10-16T07:31:27.518611Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "people.plot(kind = \"line\", x = \"body_mass_index\", y = [\"height\", \"weight\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "맷플롯립의 함수가 지원하는 다른 매개변수를 사용할 수 있습니다. 예를 들어, 산점도를 그릴 때 맷플롯립의 `scatter()` 함수의 `s` 매개변수를 사용해 크기를 지정할 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:27.558226Z",
     "iopub.status.busy": "2020-10-16T07:31:27.556557Z",
     "iopub.status.idle": "2020-10-16T07:31:27.738758Z",
     "shell.execute_reply": "2020-10-16T07:31:27.737322Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "people.plot(kind = \"scatter\", x = \"height\", y = \"weight\", s=[40, 120, 200])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "선택할 수 있는 옵션이 많습니다. 판다스 문서의 [시각화](http://pandas.pydata.org/pandas-docs/stable/visualization.html) 페이지에서 마음에 드는 그래프를 찾아 예제 코드를 살펴 보세요."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `DataFrame` 연산\n",
    "\n",
    "`DataFrame`이 넘파이 배열을 흉내내려는 것은 아니지만 몇 가지 비슷한 점이 있습니다. 예제 `DataFrame`을 만들어 보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:27.757668Z",
     "iopub.status.busy": "2020-10-16T07:31:27.756294Z",
     "iopub.status.idle": "2020-10-16T07:31:27.761443Z",
     "shell.execute_reply": "2020-10-16T07:31:27.762642Z"
    }
   },
   "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>sep</th>\n",
       "      <th>oct</th>\n",
       "      <th>nov</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         sep  oct  nov\n",
       "alice      8    8    9\n",
       "bob       10    9    9\n",
       "charles    4    8    2\n",
       "darwin     9   10   10"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades_array = np.array([[8,8,9],[10,9,9],[4, 8, 2], [9, 10, 10]])\n",
    "grades = pd.DataFrame(grades_array, columns=[\"sep\", \"oct\", \"nov\"], index=[\"alice\",\"bob\",\"charles\",\"darwin\"])\n",
    "grades"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`DataFrame`에 넘파이 수학 함수를 적용하면 모든 값에 이 함수가 적용됩니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:27.780212Z",
     "iopub.status.busy": "2020-10-16T07:31:27.778767Z",
     "iopub.status.idle": "2020-10-16T07:31:27.783503Z",
     "shell.execute_reply": "2020-10-16T07:31:27.784467Z"
    }
   },
   "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>sep</th>\n",
       "      <th>oct</th>\n",
       "      <th>nov</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>2.828427</td>\n",
       "      <td>2.828427</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>3.162278</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.828427</td>\n",
       "      <td>1.414214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.162278</td>\n",
       "      <td>3.162278</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              sep       oct       nov\n",
       "alice    2.828427  2.828427  3.000000\n",
       "bob      3.162278  3.000000  3.000000\n",
       "charles  2.000000  2.828427  1.414214\n",
       "darwin   3.000000  3.162278  3.162278"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(grades)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "비슷하게 `DataFrame`에 하나의 값을 더하면 `DataFrame`의 모든 원소에 이 값이 더해집니다. 이를 *브로드캐스팅*이라고 합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:27.798080Z",
     "iopub.status.busy": "2020-10-16T07:31:27.796749Z",
     "iopub.status.idle": "2020-10-16T07:31:27.801579Z",
     "shell.execute_reply": "2020-10-16T07:31:27.802616Z"
    }
   },
   "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>sep</th>\n",
       "      <th>oct</th>\n",
       "      <th>nov</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         sep  oct  nov\n",
       "alice      9    9   10\n",
       "bob       11   10   10\n",
       "charles    5    9    3\n",
       "darwin    10   11   11"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "물론 산술 연산(`*`,`/`,`**`...)과 조건 연산(`>`, `==`...)을 포함해 모든 이항 연산에도 마찬가지 입니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:27.817148Z",
     "iopub.status.busy": "2020-10-16T07:31:27.815851Z",
     "iopub.status.idle": "2020-10-16T07:31:27.820194Z",
     "shell.execute_reply": "2020-10-16T07:31:27.821180Z"
    },
    "scrolled": false
   },
   "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>sep</th>\n",
       "      <th>oct</th>\n",
       "      <th>nov</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           sep   oct    nov\n",
       "alice     True  True   True\n",
       "bob       True  True   True\n",
       "charles  False  True  False\n",
       "darwin    True  True   True"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades >= 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`DataFrame`의 `max`, `sum`, `mean` 같은 집계 연산은 각 열에 적용되어 `Series` 객체가 반환됩니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:27.831044Z",
     "iopub.status.busy": "2020-10-16T07:31:27.829879Z",
     "iopub.status.idle": "2020-10-16T07:31:27.835347Z",
     "shell.execute_reply": "2020-10-16T07:31:27.834398Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sep    7.75\n",
       "oct    8.75\n",
       "nov    7.50\n",
       "dtype: float64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`all` 메서드도 집계 연산입니다: 모든 값이 `True`인지 아닌지 확인합니다. 모든 학생의 점수가 `5` 이상인 월을 찾아 보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:27.845808Z",
     "iopub.status.busy": "2020-10-16T07:31:27.844575Z",
     "iopub.status.idle": "2020-10-16T07:31:27.849198Z",
     "shell.execute_reply": "2020-10-16T07:31:27.850237Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sep    False\n",
       "oct     True\n",
       "nov    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(grades > 5).all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of these functions take an optional `axis` parameter which lets you specify along which axis of the `DataFrame` you want the operation executed. The default is `axis=0`, meaning that the operation is executed vertically (on each column). You can set `axis=1` to execute the operation horizontally (on each row). For example, let's find out which students had all grades greater than `5`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:27.860089Z",
     "iopub.status.busy": "2020-10-16T07:31:27.858925Z",
     "iopub.status.idle": "2020-10-16T07:31:27.863574Z",
     "shell.execute_reply": "2020-10-16T07:31:27.864558Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "alice       True\n",
       "bob         True\n",
       "charles    False\n",
       "darwin      True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(grades > 5).all(axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`any` 메서드는 하나라도 참이면 `True`를 반환합니다. 한 번이라도 10점을 받은 사람을 찾아 보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:27.875152Z",
     "iopub.status.busy": "2020-10-16T07:31:27.873989Z",
     "iopub.status.idle": "2020-10-16T07:31:27.878564Z",
     "shell.execute_reply": "2020-10-16T07:31:27.879476Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "alice      False\n",
       "bob         True\n",
       "charles    False\n",
       "darwin      True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(grades == 10).any(axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`DataFrame`에 `Series` 객체를 더하면 (또는 다른 이항 연산을 수행하면) 판다스는 `DataFrame`에 있는 모든 *행*에 이 연산을 브로드캐스팅합니다. 이는 `Series` 객체가 `DataFrame`의 행의 개수와 크기가 같을 때만 동작합니다. 예를 들어 `DataFrame`의 `mean`(`Series` 객체)을 빼보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:27.896652Z",
     "iopub.status.busy": "2020-10-16T07:31:27.895549Z",
     "iopub.status.idle": "2020-10-16T07:31:27.900125Z",
     "shell.execute_reply": "2020-10-16T07:31:27.901252Z"
    }
   },
   "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>sep</th>\n",
       "      <th>oct</th>\n",
       "      <th>nov</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>0.25</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>2.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>-3.75</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>-5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>1.25</td>\n",
       "      <td>1.25</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          sep   oct  nov\n",
       "alice    0.25 -0.75  1.5\n",
       "bob      2.25  0.25  1.5\n",
       "charles -3.75 -0.75 -5.5\n",
       "darwin   1.25  1.25  2.5"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades - grades.mean()  # grades - [7.75, 8.75, 7.50] 와 동일"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "모든 9월 성적에서 `7.75`를 빼고, 10월 성적에서 `8.75`를 빼고, 11월 성적에서 `7.50`을 뺍니다. 이는 다음 `DataFrame`을 빼는 것과 같습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:27.919376Z",
     "iopub.status.busy": "2020-10-16T07:31:27.918032Z",
     "iopub.status.idle": "2020-10-16T07:31:27.922814Z",
     "shell.execute_reply": "2020-10-16T07:31:27.923850Z"
    }
   },
   "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>sep</th>\n",
       "      <th>oct</th>\n",
       "      <th>nov</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>7.75</td>\n",
       "      <td>8.75</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>7.75</td>\n",
       "      <td>8.75</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>7.75</td>\n",
       "      <td>8.75</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>7.75</td>\n",
       "      <td>8.75</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          sep   oct  nov\n",
       "alice    7.75  8.75  7.5\n",
       "bob      7.75  8.75  7.5\n",
       "charles  7.75  8.75  7.5\n",
       "darwin   7.75  8.75  7.5"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([[7.75, 8.75, 7.50]]*4, index=grades.index, columns=grades.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "모든 성적의 전체 평균을 빼고 싶다면 다음과 같은 방법을 사용합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:27.943417Z",
     "iopub.status.busy": "2020-10-16T07:31:27.942164Z",
     "iopub.status.idle": "2020-10-16T07:31:27.946771Z",
     "shell.execute_reply": "2020-10-16T07:31:27.947804Z"
    },
    "scrolled": true
   },
   "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>sep</th>\n",
       "      <th>oct</th>\n",
       "      <th>nov</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>-4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         sep  oct  nov\n",
       "alice    0.0  0.0  1.0\n",
       "bob      2.0  1.0  1.0\n",
       "charles -4.0  0.0 -6.0\n",
       "darwin   1.0  2.0  2.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades - grades.values.mean() # 모든 점수에서 전체 평균(8.00)을 뺍니다"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 자동 정렬\n",
    "\n",
    "`Series`와 비슷하게 여러 개의 `DataFrame`에 대한 연산을 수행하면 판다스는 자동으로 행 인덱스 레이블로 정렬하지만 열 이름으로도 정렬할 수 있습니다. 10월부터 12월까지 보너스 포인트를 담은 `DataFrame`을 만들어 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:27.967853Z",
     "iopub.status.busy": "2020-10-16T07:31:27.966587Z",
     "iopub.status.idle": "2020-10-16T07:31:27.971328Z",
     "shell.execute_reply": "2020-10-16T07:31:27.972440Z"
    }
   },
   "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>oct</th>\n",
       "      <th>nov</th>\n",
       "      <th>dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>colin</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         oct  nov  dec\n",
       "bob      0.0  NaN  2.0\n",
       "colin    NaN  1.0  0.0\n",
       "darwin   0.0  1.0  0.0\n",
       "charles  3.0  3.0  0.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bonus_array = np.array([[0,np.nan,2],[np.nan,1,0],[0, 1, 0], [3, 3, 0]])\n",
    "bonus_points = pd.DataFrame(bonus_array, columns=[\"oct\", \"nov\", \"dec\"], index=[\"bob\",\"colin\", \"darwin\", \"charles\"])\n",
    "bonus_points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:27.995052Z",
     "iopub.status.busy": "2020-10-16T07:31:27.993703Z",
     "iopub.status.idle": "2020-10-16T07:31:28.000013Z",
     "shell.execute_reply": "2020-10-16T07:31:28.001109Z"
    },
    "scrolled": true
   },
   "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",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dec</th>\n",
       "      <th>nov</th>\n",
       "      <th>oct</th>\n",
       "      <th>sep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>colin</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         dec   nov   oct  sep\n",
       "alice    NaN   NaN   NaN  NaN\n",
       "bob      NaN   NaN   9.0  NaN\n",
       "charles  NaN   5.0  11.0  NaN\n",
       "colin    NaN   NaN   NaN  NaN\n",
       "darwin   NaN  11.0  10.0  NaN"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades + bonus_points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "덧셈 연산이 수행되었지만 너무 많은 원소가 `NaN`이 되었습니다. `DataFrame`을 정렬할 때 일부 열과 행이 한 쪽에만 있기 때문입니다. 다른 쪽에는 누란되었다고 간주합니다(`NaN`). `NaN`에 어떤 수를 더하면 `NaN`이 됩니다.\n",
    "\n",
    "## 누락된 데이터 다루기\n",
    "\n",
    "실제 데이터에서 누락된 데이터를 다루는 경우는 자주 발생합니다. 판다스는 누락된 데이터를 다룰 수 있는 몇 가지 방법을 제공합니다.\n",
    "\n",
    "위 데이터에 있는 문제를 해결해 보죠. 예를 들어, 누락된 데이터는 `NaN`이 아니라 0이 되어야 한다고 결정할 수 있습니다. `fillna()` 메서드를 사용해 모든 `NaN` 값을 어떤 값으로 바꿀 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:28.031419Z",
     "iopub.status.busy": "2020-10-16T07:31:28.030067Z",
     "iopub.status.idle": "2020-10-16T07:31:28.036381Z",
     "shell.execute_reply": "2020-10-16T07:31:28.035045Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dec</th>\n",
       "      <th>nov</th>\n",
       "      <th>oct</th>\n",
       "      <th>sep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>colin</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         dec   nov   oct  sep\n",
       "alice    0.0   0.0   0.0  0.0\n",
       "bob      0.0   0.0   9.0  0.0\n",
       "charles  0.0   5.0  11.0  0.0\n",
       "colin    0.0   0.0   0.0  0.0\n",
       "darwin   0.0  11.0  10.0  0.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(grades + bonus_points).fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "9월의 점수를 0으로 만드는 것은 공정하지 않습니다. 누락된 점수는 그대로 두고, 누락된 보너스 포인트는 0으로 바꿀 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:28.066978Z",
     "iopub.status.busy": "2020-10-16T07:31:28.065621Z",
     "iopub.status.idle": "2020-10-16T07:31:28.072091Z",
     "shell.execute_reply": "2020-10-16T07:31:28.070746Z"
    }
   },
   "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>dec</th>\n",
       "      <th>nov</th>\n",
       "      <th>oct</th>\n",
       "      <th>sep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>colin</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         dec   nov   oct   sep\n",
       "alice    NaN   9.0   8.0   8.0\n",
       "bob      NaN   9.0   9.0  10.0\n",
       "charles  NaN   5.0  11.0   4.0\n",
       "colin    NaN   NaN   NaN   NaN\n",
       "darwin   NaN  11.0  10.0   9.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fixed_bonus_points = bonus_points.fillna(0)\n",
    "fixed_bonus_points.insert(0, \"sep\", 0)\n",
    "fixed_bonus_points.loc[\"alice\"] = 0\n",
    "grades + fixed_bonus_points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "훨씬 낫네요: 일부 데이터를 꾸며냈지만 덜 불공정합니다.\n",
    "\n",
    "누락된 값을 다루는 또 다른 방법은 보간입니다. `bonus_points` `DataFrame`을 다시 보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:28.088876Z",
     "iopub.status.busy": "2020-10-16T07:31:28.087481Z",
     "iopub.status.idle": "2020-10-16T07:31:28.093651Z",
     "shell.execute_reply": "2020-10-16T07:31:28.092464Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>oct</th>\n",
       "      <th>nov</th>\n",
       "      <th>dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>colin</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         oct  nov  dec\n",
       "bob      0.0  NaN  2.0\n",
       "colin    NaN  1.0  0.0\n",
       "darwin   0.0  1.0  0.0\n",
       "charles  3.0  3.0  0.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bonus_points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`interpolate` 메서드를 사용해 보죠. 기본적으로 수직 방향(`axis=0`)으로 보간합니다. 따라서 수평으로(`axis=1`)으로 보간하도록 지정합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:28.114424Z",
     "iopub.status.busy": "2020-10-16T07:31:28.113078Z",
     "iopub.status.idle": "2020-10-16T07:31:28.119895Z",
     "shell.execute_reply": "2020-10-16T07:31:28.118779Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>oct</th>\n",
       "      <th>nov</th>\n",
       "      <th>dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>colin</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         oct  nov  dec\n",
       "bob      0.0  1.0  2.0\n",
       "colin    NaN  1.0  0.0\n",
       "darwin   0.0  1.0  0.0\n",
       "charles  3.0  3.0  0.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bonus_points.interpolate(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "bob의 보너스 포인트는 10월에 0이고 12월에 2입니다. 11월을 보간하면 평균 보너스 포인트 1을 얻습니다. colin의 보너스 포인트는 11월에 1이지만 9월에 포인트는 얼마인지 모릅니다. 따라서 보간할 수 없고 10월의 포인트는 그대로 누락된 값으로 남아 있습니다. 이를 해결하려면 보간하기 전에 9월의 보너스 포인트를 0으로 설정해야 합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:28.147938Z",
     "iopub.status.busy": "2020-10-16T07:31:28.146576Z",
     "iopub.status.idle": "2020-10-16T07:31:28.152995Z",
     "shell.execute_reply": "2020-10-16T07:31:28.151814Z"
    }
   },
   "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>sep</th>\n",
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       "      <th>dec</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>colin</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         sep  oct  nov  dec\n",
       "bob      0.0  0.0  1.0  2.0\n",
       "colin    0.0  0.5  1.0  0.0\n",
       "darwin   0.0  0.0  1.0  0.0\n",
       "charles  0.0  3.0  3.0  0.0\n",
       "alice    0.0  0.0  0.0  0.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "better_bonus_points = bonus_points.copy()\n",
    "better_bonus_points.insert(0, \"sep\", 0)\n",
    "better_bonus_points.loc[\"alice\"] = 0\n",
    "better_bonus_points = better_bonus_points.interpolate(axis=1)\n",
    "better_bonus_points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "좋습니다. 이제 모든 보너스 포인트가 합리적으로 보간되었습니다. 최종 점수를 확인해 보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:28.175899Z",
     "iopub.status.busy": "2020-10-16T07:31:28.174553Z",
     "iopub.status.idle": "2020-10-16T07:31:28.181130Z",
     "shell.execute_reply": "2020-10-16T07:31:28.179772Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>dec</th>\n",
       "      <th>nov</th>\n",
       "      <th>oct</th>\n",
       "      <th>sep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>colin</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         dec   nov   oct   sep\n",
       "alice    NaN   9.0   8.0   8.0\n",
       "bob      NaN  10.0   9.0  10.0\n",
       "charles  NaN   5.0  11.0   4.0\n",
       "colin    NaN   NaN   NaN   NaN\n",
       "darwin   NaN  11.0  10.0   9.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades + better_bonus_points"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "9월 열이 오른쪽에 추가되었는데 좀 이상합니다. 이는 더하려는 `DataFrame`이 정확히 같은 열을 가지고 있지 않기 때문입니다(`grade` `DataFrame`에는 `\"dec\"` 열이 없습니다). 따라서 판다스는 알파벳 순서로 최종 열을 정렬합니다. 이를 해결하려면 덧셈을 하기 전에 누락된 열을 추가하면 됩니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:28.202239Z",
     "iopub.status.busy": "2020-10-16T07:31:28.200876Z",
     "iopub.status.idle": "2020-10-16T07:31:28.207279Z",
     "shell.execute_reply": "2020-10-16T07:31:28.206121Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sep</th>\n",
       "      <th>oct</th>\n",
       "      <th>nov</th>\n",
       "      <th>dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>colin</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          sep   oct   nov  dec\n",
       "alice     8.0   8.0   9.0  NaN\n",
       "bob      10.0   9.0  10.0  NaN\n",
       "charles   4.0  11.0   5.0  NaN\n",
       "colin     NaN   NaN   NaN  NaN\n",
       "darwin    9.0  10.0  11.0  NaN"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades[\"dec\"] = np.nan\n",
    "final_grades = grades + better_bonus_points\n",
    "final_grades"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "12월과 colin에 대해 할 수 있는 것이 많지 않습니다. 보너스 포인트를 만드는 것이 나쁘지만 점수를 합리적으로 올릴 수는 없습니다(어떤 선생님들은 그럴 수 있지만). `dropna()` 메서드를 사용해 모두 `NaN`인 행을 삭제합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:28.226459Z",
     "iopub.status.busy": "2020-10-16T07:31:28.225200Z",
     "iopub.status.idle": "2020-10-16T07:31:28.231887Z",
     "shell.execute_reply": "2020-10-16T07:31:28.230844Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sep</th>\n",
       "      <th>oct</th>\n",
       "      <th>nov</th>\n",
       "      <th>dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          sep   oct   nov  dec\n",
       "alice     8.0   8.0   9.0  NaN\n",
       "bob      10.0   9.0  10.0  NaN\n",
       "charles   4.0  11.0   5.0  NaN\n",
       "darwin    9.0  10.0  11.0  NaN"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_grades_clean = final_grades.dropna(how=\"all\")\n",
    "final_grades_clean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "그다음 `axis` 매개변수를 `1`로 지정하여 모두 `NaN`인 열을 삭제합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:28.249932Z",
     "iopub.status.busy": "2020-10-16T07:31:28.248633Z",
     "iopub.status.idle": "2020-10-16T07:31:28.255413Z",
     "shell.execute_reply": "2020-10-16T07:31:28.254162Z"
    }
   },
   "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>sep</th>\n",
       "      <th>oct</th>\n",
       "      <th>nov</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          sep   oct   nov\n",
       "alice     8.0   8.0   9.0\n",
       "bob      10.0   9.0  10.0\n",
       "charles   4.0  11.0   5.0\n",
       "darwin    9.0  10.0  11.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_grades_clean = final_grades_clean.dropna(axis=1, how=\"all\")\n",
    "final_grades_clean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `groupby`로 집계하기\n",
    "\n",
    "SQL과 비슷하게 판다스는 데이터를 그룹핑하고 각 그룹에 대해 연산을 수행할 수 있습니다.\n",
    "\n",
    "먼저 그루핑을 위해 각 사람의 데이터를 추가로 만들겠습니다. `NaN` 값을 어떻게 다루는지 보기 위해 `final_grades` `DataFrame`을 다시 사용하겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:28.275015Z",
     "iopub.status.busy": "2020-10-16T07:31:28.273759Z",
     "iopub.status.idle": "2020-10-16T07:31:28.280854Z",
     "shell.execute_reply": "2020-10-16T07:31:28.279781Z"
    },
    "scrolled": true
   },
   "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>sep</th>\n",
       "      <th>oct</th>\n",
       "      <th>nov</th>\n",
       "      <th>dec</th>\n",
       "      <th>hobby</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Biking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dancing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>colin</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dancing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Biking</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          sep   oct   nov  dec    hobby\n",
       "alice     8.0   8.0   9.0  NaN   Biking\n",
       "bob      10.0   9.0  10.0  NaN  Dancing\n",
       "charles   4.0  11.0   5.0  NaN      NaN\n",
       "colin     NaN   NaN   NaN  NaN  Dancing\n",
       "darwin    9.0  10.0  11.0  NaN   Biking"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_grades[\"hobby\"] = [\"Biking\", \"Dancing\", np.nan, \"Dancing\", \"Biking\"]\n",
    "final_grades"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "hobby로 이 `DataFrame`을 그룹핑해 보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:28.289508Z",
     "iopub.status.busy": "2020-10-16T07:31:28.288213Z",
     "iopub.status.idle": "2020-10-16T07:31:28.293911Z",
     "shell.execute_reply": "2020-10-16T07:31:28.294840Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7fccd01f18d0>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_grades = final_grades.groupby(\"hobby\")\n",
    "grouped_grades"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이제 hobby마다 평균 점수를 계산할 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:28.315872Z",
     "iopub.status.busy": "2020-10-16T07:31:28.314695Z",
     "iopub.status.idle": "2020-10-16T07:31:28.320059Z",
     "shell.execute_reply": "2020-10-16T07:31:28.321150Z"
    }
   },
   "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>sep</th>\n",
       "      <th>oct</th>\n",
       "      <th>nov</th>\n",
       "      <th>dec</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hobby</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Biking</th>\n",
       "      <td>8.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dancing</th>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          sep  oct   nov  dec\n",
       "hobby                        \n",
       "Biking    8.5  9.0  10.0  NaN\n",
       "Dancing  10.0  9.0  10.0  NaN"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_grades.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "아주 쉽네요! 평균을 계산할 때 `NaN` 값은 그냥 무시됩니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 피봇 테이블\n",
    "\n",
    "판다스는 스프레드시트와 비슷하 [피봇 테이블](https://en.wikipedia.org/wiki/Pivot_table)을 지원하여 데이터를 빠르게 요약할 수 있습니다. 어떻게 동작하는 알아 보기 위해 간단한 `DataFrame`을 만들어 보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:28.337894Z",
     "iopub.status.busy": "2020-10-16T07:31:28.336559Z",
     "iopub.status.idle": "2020-10-16T07:31:28.342840Z",
     "shell.execute_reply": "2020-10-16T07:31:28.341758Z"
    }
   },
   "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>oct</th>\n",
       "      <th>nov</th>\n",
       "      <th>dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>colin</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         oct  nov  dec\n",
       "bob      0.0  NaN  2.0\n",
       "colin    NaN  1.0  0.0\n",
       "darwin   0.0  1.0  0.0\n",
       "charles  3.0  3.0  0.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bonus_points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:28.368578Z",
     "iopub.status.busy": "2020-10-16T07:31:28.367317Z",
     "iopub.status.idle": "2020-10-16T07:31:28.374218Z",
     "shell.execute_reply": "2020-10-16T07:31:28.373063Z"
    }
   },
   "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>name</th>\n",
       "      <th>month</th>\n",
       "      <th>grade</th>\n",
       "      <th>bonus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>alice</td>\n",
       "      <td>sep</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>alice</td>\n",
       "      <td>oct</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>alice</td>\n",
       "      <td>nov</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bob</td>\n",
       "      <td>sep</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bob</td>\n",
       "      <td>oct</td>\n",
       "      <td>9.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bob</td>\n",
       "      <td>nov</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>charles</td>\n",
       "      <td>sep</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>charles</td>\n",
       "      <td>oct</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>charles</td>\n",
       "      <td>nov</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>darwin</td>\n",
       "      <td>sep</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>darwin</td>\n",
       "      <td>oct</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>darwin</td>\n",
       "      <td>nov</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       name month  grade  bonus\n",
       "0     alice   sep    8.0    NaN\n",
       "1     alice   oct    8.0    NaN\n",
       "2     alice   nov    9.0    NaN\n",
       "3       bob   sep   10.0    0.0\n",
       "4       bob   oct    9.0    NaN\n",
       "5       bob   nov   10.0    2.0\n",
       "6   charles   sep    4.0    3.0\n",
       "7   charles   oct   11.0    3.0\n",
       "8   charles   nov    5.0    0.0\n",
       "9    darwin   sep    9.0    0.0\n",
       "10   darwin   oct   10.0    1.0\n",
       "11   darwin   nov   11.0    0.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "more_grades = final_grades_clean.stack().reset_index()\n",
    "more_grades.columns = [\"name\", \"month\", \"grade\"]\n",
    "more_grades[\"bonus\"] = [np.nan, np.nan, np.nan, 0, np.nan, 2, 3, 3, 0, 0, 1, 0]\n",
    "more_grades"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이제 이 `DataFrame`에 대해 `pd.pivot_table()` 함수를 호출하고 `name` 열로 그룹핑합니다. 기본적으로 `pivot_table()`은 수치 열의 평균을 계산합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:28.396388Z",
     "iopub.status.busy": "2020-10-16T07:31:28.384014Z",
     "iopub.status.idle": "2020-10-16T07:31:28.402172Z",
     "shell.execute_reply": "2020-10-16T07:31:28.403481Z"
    }
   },
   "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>bonus</th>\n",
       "      <th>grade</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            bonus      grade\n",
       "name                        \n",
       "alice         NaN   8.333333\n",
       "bob      1.000000   9.666667\n",
       "charles  2.000000   6.666667\n",
       "darwin   0.333333  10.000000"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(more_grades, index=\"name\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "집계 함수를 `aggfunc` 매개변수로 바꿀 수 있습니다. 또한 집계 대상의 열을 리스트로 지정할 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:28.429813Z",
     "iopub.status.busy": "2020-10-16T07:31:28.428152Z",
     "iopub.status.idle": "2020-10-16T07:31:28.433948Z",
     "shell.execute_reply": "2020-10-16T07:31:28.435250Z"
    }
   },
   "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>bonus</th>\n",
       "      <th>grade</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         bonus  grade\n",
       "name                 \n",
       "alice      NaN    9.0\n",
       "bob        2.0   10.0\n",
       "charles    3.0   11.0\n",
       "darwin     1.0   11.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(more_grades, index=\"name\", values=[\"grade\",\"bonus\"], aggfunc=np.max)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`columns` 매개변수를 지정하여 수평으로 집계할 수 있고 `margins=True`로 설정해 각 행과 열에 대해 전체 합을 계산할 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:28.447027Z",
     "iopub.status.busy": "2020-10-16T07:31:28.445382Z",
     "iopub.status.idle": "2020-10-16T07:31:28.501446Z",
     "shell.execute_reply": "2020-10-16T07:31:28.500477Z"
    }
   },
   "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",
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       "    }\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>month</th>\n",
       "      <th>nov</th>\n",
       "      <th>oct</th>\n",
       "      <th>sep</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>9.00</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>10.00</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>9.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>charles</th>\n",
       "      <td>5.00</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>6.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>darwin</th>\n",
       "      <td>11.00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>8.75</td>\n",
       "      <td>9.5</td>\n",
       "      <td>7.75</td>\n",
       "      <td>8.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "month      nov   oct    sep        All\n",
       "name                                  \n",
       "alice     9.00   8.0   8.00   8.333333\n",
       "bob      10.00   9.0  10.00   9.666667\n",
       "charles   5.00  11.0   4.00   6.666667\n",
       "darwin   11.00  10.0   9.00  10.000000\n",
       "All       8.75   9.5   7.75   8.666667"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(more_grades, index=\"name\", values=\"grade\", columns=\"month\", margins=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "마지막으로 여러 개의 인덱스나 열 이름을 지정하면 판다스가 다중 레벨 인덱스를 만듭니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:28.538598Z",
     "iopub.status.busy": "2020-10-16T07:31:28.516477Z",
     "iopub.status.idle": "2020-10-16T07:31:28.544991Z",
     "shell.execute_reply": "2020-10-16T07:31:28.543793Z"
    }
   },
   "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></th>\n",
       "      <th>bonus</th>\n",
       "      <th>grade</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">alice</th>\n",
       "      <th>nov</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oct</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sep</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">bob</th>\n",
       "      <th>nov</th>\n",
       "      <td>2.000</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oct</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sep</th>\n",
       "      <td>0.000</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">charles</th>\n",
       "      <th>nov</th>\n",
       "      <td>0.000</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oct</th>\n",
       "      <td>3.000</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sep</th>\n",
       "      <td>3.000</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">darwin</th>\n",
       "      <th>nov</th>\n",
       "      <td>0.000</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oct</th>\n",
       "      <td>1.000</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sep</th>\n",
       "      <td>0.000</td>\n",
       "      <td>9.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <th></th>\n",
       "      <td>1.125</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               bonus  grade\n",
       "name    month              \n",
       "alice   nov      NaN   9.00\n",
       "        oct      NaN   8.00\n",
       "        sep      NaN   8.00\n",
       "bob     nov    2.000  10.00\n",
       "        oct      NaN   9.00\n",
       "        sep    0.000  10.00\n",
       "charles nov    0.000   5.00\n",
       "        oct    3.000  11.00\n",
       "        sep    3.000   4.00\n",
       "darwin  nov    0.000  11.00\n",
       "        oct    1.000  10.00\n",
       "        sep    0.000   9.00\n",
       "All            1.125   8.75"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(more_grades, index=(\"name\", \"month\"), margins=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 함수\n",
    "\n",
    "큰 `DataFrame`을 다룰 때 내용을 간단히 요약하는 것이 도움이 됩니다. 판다스는 이를 위한 몇 가지 함수를 제공합니다. 먼저 수치 값, 누락된 값, 텍스트 값이 섞인 큰 `DataFrame`을 만들어 보죠. 주피터 노트북은 이 `DataFrame`의 일부만 보여줍니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:28.555609Z",
     "iopub.status.busy": "2020-10-16T07:31:28.554261Z",
     "iopub.status.idle": "2020-10-16T07:31:28.679610Z",
     "shell.execute_reply": "2020-10-16T07:31:28.678101Z"
    },
    "scrolled": false
   },
   "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>some_text</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "      <th>G</th>\n",
       "      <th>H</th>\n",
       "      <th>I</th>\n",
       "      <th>...</th>\n",
       "      <th>Q</th>\n",
       "      <th>R</th>\n",
       "      <th>S</th>\n",
       "      <th>T</th>\n",
       "      <th>U</th>\n",
       "      <th>V</th>\n",
       "      <th>W</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>99.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>143.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>110.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>154.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>154.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>121.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>165.0</td>\n",
       "      <td>...</td>\n",
       "      <td>33.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>132.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>44.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>44.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>143.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>55.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>88.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>132.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>99.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>143.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>11.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>110.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>154.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>154.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>22.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>121.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>165.0</td>\n",
       "      <td>...</td>\n",
       "      <td>33.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>33.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>Blabla</td>\n",
       "      <td>132.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>44.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         A     B     C some_text      D      E      F     G      H      I  \\\n",
       "0      NaN  11.0  44.0    Blabla   99.0    NaN   88.0  22.0  165.0  143.0   \n",
       "1     11.0  22.0  55.0    Blabla  110.0    NaN   99.0  33.0    NaN  154.0   \n",
       "2     22.0  33.0  66.0    Blabla  121.0   11.0  110.0  44.0    NaN  165.0   \n",
       "3     33.0  44.0  77.0    Blabla  132.0   22.0  121.0  55.0   11.0    NaN   \n",
       "4     44.0  55.0  88.0    Blabla  143.0   33.0  132.0  66.0   22.0    NaN   \n",
       "...    ...   ...   ...       ...    ...    ...    ...   ...    ...    ...   \n",
       "9995   NaN   NaN  33.0    Blabla   88.0  165.0   77.0  11.0  154.0  132.0   \n",
       "9996   NaN  11.0  44.0    Blabla   99.0    NaN   88.0  22.0  165.0  143.0   \n",
       "9997  11.0  22.0  55.0    Blabla  110.0    NaN   99.0  33.0    NaN  154.0   \n",
       "9998  22.0  33.0  66.0    Blabla  121.0   11.0  110.0  44.0    NaN  165.0   \n",
       "9999  33.0  44.0  77.0    Blabla  132.0   22.0  121.0  55.0   11.0    NaN   \n",
       "\n",
       "      ...     Q     R     S     T      U      V      W     X      Y      Z  \n",
       "0     ...  11.0   NaN  11.0  44.0   99.0    NaN   88.0  22.0  165.0  143.0  \n",
       "1     ...  22.0  11.0  22.0  55.0  110.0    NaN   99.0  33.0    NaN  154.0  \n",
       "2     ...  33.0  22.0  33.0  66.0  121.0   11.0  110.0  44.0    NaN  165.0  \n",
       "3     ...  44.0  33.0  44.0  77.0  132.0   22.0  121.0  55.0   11.0    NaN  \n",
       "4     ...  55.0  44.0  55.0  88.0  143.0   33.0  132.0  66.0   22.0    NaN  \n",
       "...   ...   ...   ...   ...   ...    ...    ...    ...   ...    ...    ...  \n",
       "9995  ...   NaN   NaN   NaN  33.0   88.0  165.0   77.0  11.0  154.0  132.0  \n",
       "9996  ...  11.0   NaN  11.0  44.0   99.0    NaN   88.0  22.0  165.0  143.0  \n",
       "9997  ...  22.0  11.0  22.0  55.0  110.0    NaN   99.0  33.0    NaN  154.0  \n",
       "9998  ...  33.0  22.0  33.0  66.0  121.0   11.0  110.0  44.0    NaN  165.0  \n",
       "9999  ...  44.0  33.0  44.0  77.0  132.0   22.0  121.0  55.0   11.0    NaN  \n",
       "\n",
       "[10000 rows x 27 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "much_data = np.fromfunction(lambda x,y: (x+y*y)%17*11, (10000, 26))\n",
    "large_df = pd.DataFrame(much_data, columns=list(\"ABCDEFGHIJKLMNOPQRSTUVWXYZ\"))\n",
    "large_df[large_df % 16 == 0] = np.nan\n",
    "large_df.insert(3,\"some_text\", \"Blabla\")\n",
    "large_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`head()` 메서드는 처음 5개 행을 반환합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "마지막 5개 행을 반환하는 `tail()` 함수도 있습니다. 원하는 행 개수를 전달할 수도 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
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     "shell.execute_reply": "2020-10-16T07:31:28.776741Z"
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     "execution_count": 1,
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   "source": [
    "large_df.tail(n=2)"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`info()` 메서드는 각 열의 내용을 요약하여 출력합니다:"
   ]
  },
  {
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   "execution_count": 127,
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 27 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   A          8823 non-null   float64\n",
      " 1   B          8824 non-null   float64\n",
      " 2   C          8824 non-null   float64\n",
      " 3   some_text  10000 non-null  object \n",
      " 4   D          8824 non-null   float64\n",
      " 5   E          8822 non-null   float64\n",
      " 6   F          8824 non-null   float64\n",
      " 7   G          8824 non-null   float64\n",
      " 8   H          8822 non-null   float64\n",
      " 9   I          8823 non-null   float64\n",
      " 10  J          8823 non-null   float64\n",
      " 11  K          8822 non-null   float64\n",
      " 12  L          8824 non-null   float64\n",
      " 13  M          8824 non-null   float64\n",
      " 14  N          8822 non-null   float64\n",
      " 15  O          8824 non-null   float64\n",
      " 16  P          8824 non-null   float64\n",
      " 17  Q          8824 non-null   float64\n",
      " 18  R          8823 non-null   float64\n",
      " 19  S          8824 non-null   float64\n",
      " 20  T          8824 non-null   float64\n",
      " 21  U          8824 non-null   float64\n",
      " 22  V          8822 non-null   float64\n",
      " 23  W          8824 non-null   float64\n",
      " 24  X          8824 non-null   float64\n",
      " 25  Y          8822 non-null   float64\n",
      " 26  Z          8823 non-null   float64\n",
      "dtypes: float64(26), object(1)\n",
      "memory usage: 2.1+ MB\n"
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    "large_df.info()"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "마지막으로 `describe()` 메서드는 각 열에 대한 주요 집계 연산을 수행한 결과를 보여줍니다:\n",
    "\n",
    "Finally, the `describe()` method gives a nice overview of the main aggregated values over each column:\n",
    "* `count`: null(NaN)이 아닌 값의 개수\n",
    "* `mean`: null이 아닌 값의 평균\n",
    "* `std`: null이 아닌 값의 [표준 편차](https://ko.wikipedia.org/wiki/%ED%91%9C%EC%A4%80_%ED%8E%B8%EC%B0%A8)\n",
    "* `min`: null이 아닌 값의 최솟값\n",
    "* `25%`, `50%`, `75%`: null이 아닌 값의 25번째, 50번째, 75번째 [백분위수](https://ko.wikipedia.org/wiki/%EB%B0%B1%EB%B6%84%EC%9C%84%EC%88%98)\n",
    "* `max`: null이 아닌 값의 최댓값"
   ]
  },
  {
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       "      <td>47.521679</td>\n",
       "      <td>47.535001</td>\n",
       "      <td>47.519371</td>\n",
       "      <td>47.529755</td>\n",
       "      <td>47.536879</td>\n",
       "      <td>47.535911</td>\n",
       "      <td>47.535911</td>\n",
       "      <td>...</td>\n",
       "      <td>47.535523</td>\n",
       "      <td>47.535911</td>\n",
       "      <td>47.535523</td>\n",
       "      <td>47.521679</td>\n",
       "      <td>47.521679</td>\n",
       "      <td>47.535001</td>\n",
       "      <td>47.519371</td>\n",
       "      <td>47.529755</td>\n",
       "      <td>47.536879</td>\n",
       "      <td>47.535911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>88.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>132.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>132.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>165.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 A            B            C            D            E  \\\n",
       "count  8823.000000  8824.000000  8824.000000  8824.000000  8822.000000   \n",
       "mean     87.977559    87.972575    87.987534    88.012466    87.983791   \n",
       "std      47.535911    47.535523    47.521679    47.521679    47.535001   \n",
       "min      11.000000    11.000000    11.000000    11.000000    11.000000   \n",
       "25%      44.000000    44.000000    44.000000    44.000000    44.000000   \n",
       "50%      88.000000    88.000000    88.000000    88.000000    88.000000   \n",
       "75%     132.000000   132.000000   132.000000   132.000000   132.000000   \n",
       "max     165.000000   165.000000   165.000000   165.000000   165.000000   \n",
       "\n",
       "                 F            G            H            I            J  ...  \\\n",
       "count  8824.000000  8824.000000  8822.000000  8823.000000  8823.000000  ...   \n",
       "mean     88.007480    87.977561    88.000000    88.022441    88.022441  ...   \n",
       "std      47.519371    47.529755    47.536879    47.535911    47.535911  ...   \n",
       "min      11.000000    11.000000    11.000000    11.000000    11.000000  ...   \n",
       "25%      44.000000    44.000000    44.000000    44.000000    44.000000  ...   \n",
       "50%      88.000000    88.000000    88.000000    88.000000    88.000000  ...   \n",
       "75%     132.000000   132.000000   132.000000   132.000000   132.000000  ...   \n",
       "max     165.000000   165.000000   165.000000   165.000000   165.000000  ...   \n",
       "\n",
       "                 Q            R            S            T            U  \\\n",
       "count  8824.000000  8823.000000  8824.000000  8824.000000  8824.000000   \n",
       "mean     87.972575    87.977559    87.972575    87.987534    88.012466   \n",
       "std      47.535523    47.535911    47.535523    47.521679    47.521679   \n",
       "min      11.000000    11.000000    11.000000    11.000000    11.000000   \n",
       "25%      44.000000    44.000000    44.000000    44.000000    44.000000   \n",
       "50%      88.000000    88.000000    88.000000    88.000000    88.000000   \n",
       "75%     132.000000   132.000000   132.000000   132.000000   132.000000   \n",
       "max     165.000000   165.000000   165.000000   165.000000   165.000000   \n",
       "\n",
       "                 V            W            X            Y            Z  \n",
       "count  8822.000000  8824.000000  8824.000000  8822.000000  8823.000000  \n",
       "mean     87.983791    88.007480    87.977561    88.000000    88.022441  \n",
       "std      47.535001    47.519371    47.529755    47.536879    47.535911  \n",
       "min      11.000000    11.000000    11.000000    11.000000    11.000000  \n",
       "25%      44.000000    44.000000    44.000000    44.000000    44.000000  \n",
       "50%      88.000000    88.000000    88.000000    88.000000    88.000000  \n",
       "75%     132.000000   132.000000   132.000000   132.000000   132.000000  \n",
       "max     165.000000   165.000000   165.000000   165.000000   165.000000  \n",
       "\n",
       "[8 rows x 26 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "large_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 저장 & 로딩\n",
    "\n",
    "판다스는 `DataFrame`를 여러 가지 포맷으로 저장할 수 있습니다. CSV, Excel, JSON, HTML, HDF5, SQL 데이터베이스 같은 포맷이 가능합니다. 예제를 위해 `DataFrame`을 하나 만들어 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:29.000434Z",
     "iopub.status.busy": "2020-10-16T07:31:28.999329Z",
     "iopub.status.idle": "2020-10-16T07:31:29.004808Z",
     "shell.execute_reply": "2020-10-16T07:31:29.005902Z"
    }
   },
   "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>hobby</th>\n",
       "      <th>weight</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>children</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>Biking</td>\n",
       "      <td>68.5</td>\n",
       "      <td>1985</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>Dancing</td>\n",
       "      <td>83.1</td>\n",
       "      <td>1984</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         hobby  weight  birthyear  children\n",
       "alice   Biking    68.5       1985       NaN\n",
       "bob    Dancing    83.1       1984       3.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_df = pd.DataFrame(\n",
    "    [[\"Biking\", 68.5, 1985, np.nan], [\"Dancing\", 83.1, 1984, 3]], \n",
    "    columns=[\"hobby\",\"weight\",\"birthyear\",\"children\"],\n",
    "    index=[\"alice\", \"bob\"]\n",
    ")\n",
    "my_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 저장\n",
    "\n",
    "CSV, HTML, JSON로 저장해 보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:29.014207Z",
     "iopub.status.busy": "2020-10-16T07:31:29.013037Z",
     "iopub.status.idle": "2020-10-16T07:31:29.057312Z",
     "shell.execute_reply": "2020-10-16T07:31:29.056173Z"
    }
   },
   "outputs": [],
   "source": [
    "my_df.to_csv(\"my_df.csv\")\n",
    "my_df.to_html(\"my_df.html\")\n",
    "my_df.to_json(\"my_df.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "저장된 내용을 확인해 보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:29.067993Z",
     "iopub.status.busy": "2020-10-16T07:31:29.066881Z",
     "iopub.status.idle": "2020-10-16T07:31:29.081991Z",
     "shell.execute_reply": "2020-10-16T07:31:29.080808Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# my_df.csv\n",
      ",hobby,weight,birthyear,children\n",
      "alice,Biking,68.5,1985,\n",
      "bob,Dancing,83.1,1984,3.0\n",
      "\n",
      "\n",
      "# my_df.html\n",
      "<table border=\"1\" class=\"dataframe\">\n",
      "  <thead>\n",
      "    <tr style=\"text-align: right;\">\n",
      "      <th></th>\n",
      "      <th>hobby</th>\n",
      "      <th>weight</th>\n",
      "      <th>birthyear</th>\n",
      "      <th>children</th>\n",
      "    </tr>\n",
      "  </thead>\n",
      "  <tbody>\n",
      "    <tr>\n",
      "      <th>alice</th>\n",
      "      <td>Biking</td>\n",
      "      <td>68.5</td>\n",
      "      <td>1985</td>\n",
      "      <td>NaN</td>\n",
      "    </tr>\n",
      "    <tr>\n",
      "      <th>bob</th>\n",
      "      <td>Dancing</td>\n",
      "      <td>83.1</td>\n",
      "      <td>1984</td>\n",
      "      <td>3.0</td>\n",
      "    </tr>\n",
      "  </tbody>\n",
      "</table>\n",
      "\n",
      "# my_df.json\n",
      "{\"hobby\":{\"alice\":\"Biking\",\"bob\":\"Dancing\"},\"weight\":{\"alice\":68.5,\"bob\":83.1},\"birthyear\":{\"alice\":1985,\"bob\":1984},\"children\":{\"alice\":null,\"bob\":3.0}}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for filename in (\"my_df.csv\", \"my_df.html\", \"my_df.json\"):\n",
    "    print(\"#\", filename)\n",
    "    with open(filename, \"rt\") as f:\n",
    "        print(f.read())\n",
    "        print()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "인덱스는 (이름 없이) CSV 파일의 첫 번째 열에 저장되었습니다. HTML에서는 `<th>` 태그와 JSON에서는 키로 저장되었습니다.\n",
    "\n",
    "다른 포맷으로 저장하는 것도 비슷합니다. 하지만 일부 포맷은 추가적인 라이브러리 설치가 필요합니다. 예를 들어, 엑셀로 저장하려면 openpyxl 라이브러리가 필요합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:29.092332Z",
     "iopub.status.busy": "2020-10-16T07:31:29.090986Z",
     "iopub.status.idle": "2020-10-16T07:31:29.098706Z",
     "shell.execute_reply": "2020-10-16T07:31:29.097724Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No module named 'openpyxl'\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    my_df.to_excel(\"my_df.xlsx\", sheet_name='People')\n",
    "except ImportError as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 로딩\n",
    "\n",
    "CSV 파일을 `DataFrame`으로 로드해 보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:29.107752Z",
     "iopub.status.busy": "2020-10-16T07:31:29.106586Z",
     "iopub.status.idle": "2020-10-16T07:31:29.126178Z",
     "shell.execute_reply": "2020-10-16T07:31:29.124769Z"
    }
   },
   "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>hobby</th>\n",
       "      <th>weight</th>\n",
       "      <th>birthyear</th>\n",
       "      <th>children</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alice</th>\n",
       "      <td>Biking</td>\n",
       "      <td>68.5</td>\n",
       "      <td>1985</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bob</th>\n",
       "      <td>Dancing</td>\n",
       "      <td>83.1</td>\n",
       "      <td>1984</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         hobby  weight  birthyear  children\n",
       "alice   Biking    68.5       1985       NaN\n",
       "bob    Dancing    83.1       1984       3.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_df_loaded = pd.read_csv(\"my_df.csv\", index_col=0)\n",
    "my_df_loaded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "예상할 수 있듯이 `read_json`, `read_html`, `read_excel` 함수도 있습니다. 인터넷에서 데이터를 바로 읽을 수도 있습니다. 예를 들어 깃허브에서 1,000개의 U.S. 도시를 로드해 보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:29.140192Z",
     "iopub.status.busy": "2020-10-16T07:31:29.138604Z",
     "iopub.status.idle": "2020-10-16T07:31:29.323556Z",
     "shell.execute_reply": "2020-10-16T07:31:29.322579Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HTTP Error 403: Forbidden\n"
     ]
    }
   ],
   "source": [
    "us_cities = None\n",
    "try:\n",
    "    csv_url = \"https://raw.githubusercontent.com/plotly/datasets/master/us-cities-top-1k.csv\"\n",
    "    us_cities = pd.read_csv(csv_url, index_col=0)\n",
    "    us_cities = us_cities.head()\n",
    "except IOError as e:\n",
    "    print(e)\n",
    "us_cities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이외에도 많은 옵션이 있습니다. 특히 datetime 포맷에 관련된 옵션이 많습니다. 더 자세한 내용은 온라인 [문서](http://pandas.pydata.org/pandas-docs/stable/io.html)를 참고하세요."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `DataFrame` 합치기\n",
    "\n",
    "## SQL 조인\n",
    "\n",
    "판다스의 강력한 기능 중 하나는 `DataFrame`에 대해 SQL 같은 조인(join)을 수행할 수 있는 것입니다. 여러 종류의 조인이 지원됩니다. 이너 조인(inner join), 레프트/라이트 아우터 조인(left/right outer join), 풀 조인(full join)입니다. 이에 대해 알아 보기 위해 간단한 `DataFrame`을 만들어 보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:29.346066Z",
     "iopub.status.busy": "2020-10-16T07:31:29.344663Z",
     "iopub.status.idle": "2020-10-16T07:31:29.351634Z",
     "shell.execute_reply": "2020-10-16T07:31:29.350373Z"
    }
   },
   "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>state</th>\n",
       "      <th>city</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>37.781334</td>\n",
       "      <td>-122.416728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NY</td>\n",
       "      <td>New York</td>\n",
       "      <td>40.705649</td>\n",
       "      <td>-74.008344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FL</td>\n",
       "      <td>Miami</td>\n",
       "      <td>25.791100</td>\n",
       "      <td>-80.320733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OH</td>\n",
       "      <td>Cleveland</td>\n",
       "      <td>41.473508</td>\n",
       "      <td>-81.739791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UT</td>\n",
       "      <td>Salt Lake City</td>\n",
       "      <td>40.755851</td>\n",
       "      <td>-111.896657</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state            city        lat         lng\n",
       "0    CA   San Francisco  37.781334 -122.416728\n",
       "1    NY        New York  40.705649  -74.008344\n",
       "2    FL           Miami  25.791100  -80.320733\n",
       "3    OH       Cleveland  41.473508  -81.739791\n",
       "4    UT  Salt Lake City  40.755851 -111.896657"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_loc = pd.DataFrame(\n",
    "    [\n",
    "        [\"CA\", \"San Francisco\", 37.781334, -122.416728],\n",
    "        [\"NY\", \"New York\", 40.705649, -74.008344],\n",
    "        [\"FL\", \"Miami\", 25.791100, -80.320733],\n",
    "        [\"OH\", \"Cleveland\", 41.473508, -81.739791],\n",
    "        [\"UT\", \"Salt Lake City\", 40.755851, -111.896657]\n",
    "    ], columns=[\"state\", \"city\", \"lat\", \"lng\"])\n",
    "city_loc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:29.372130Z",
     "iopub.status.busy": "2020-10-16T07:31:29.370659Z",
     "iopub.status.idle": "2020-10-16T07:31:29.376662Z",
     "shell.execute_reply": "2020-10-16T07:31:29.375633Z"
    }
   },
   "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>population</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>808976</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8363710</td>\n",
       "      <td>New York</td>\n",
       "      <td>New-York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>413201</td>\n",
       "      <td>Miami</td>\n",
       "      <td>Florida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2242193</td>\n",
       "      <td>Houston</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   population           city       state\n",
       "3      808976  San Francisco  California\n",
       "4     8363710       New York    New-York\n",
       "5      413201          Miami     Florida\n",
       "6     2242193        Houston       Texas"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_pop = pd.DataFrame(\n",
    "    [\n",
    "        [808976, \"San Francisco\", \"California\"],\n",
    "        [8363710, \"New York\", \"New-York\"],\n",
    "        [413201, \"Miami\", \"Florida\"],\n",
    "        [2242193, \"Houston\", \"Texas\"]\n",
    "    ], index=[3,4,5,6], columns=[\"population\", \"city\", \"state\"])\n",
    "city_pop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이제 `merge()` 함수를 사용해 이 `DataFrame`을 조인해 보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:29.401493Z",
     "iopub.status.busy": "2020-10-16T07:31:29.400088Z",
     "iopub.status.idle": "2020-10-16T07:31:29.407147Z",
     "shell.execute_reply": "2020-10-16T07:31:29.405772Z"
    }
   },
   "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>state_x</th>\n",
       "      <th>city</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>population</th>\n",
       "      <th>state_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>37.781334</td>\n",
       "      <td>-122.416728</td>\n",
       "      <td>808976</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NY</td>\n",
       "      <td>New York</td>\n",
       "      <td>40.705649</td>\n",
       "      <td>-74.008344</td>\n",
       "      <td>8363710</td>\n",
       "      <td>New-York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FL</td>\n",
       "      <td>Miami</td>\n",
       "      <td>25.791100</td>\n",
       "      <td>-80.320733</td>\n",
       "      <td>413201</td>\n",
       "      <td>Florida</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state_x           city        lat         lng  population     state_y\n",
       "0      CA  San Francisco  37.781334 -122.416728      808976  California\n",
       "1      NY       New York  40.705649  -74.008344     8363710    New-York\n",
       "2      FL          Miami  25.791100  -80.320733      413201     Florida"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left=city_loc, right=city_pop, on=\"city\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "두 `DataFrame`은 `state`란 이름의 열을 가지고 있으므로 `state_x`와 `state_y`로 이름이 바뀌었습니다.\n",
    "\n",
    "또한 Cleveland, Salt Lake City, Houston은 두 `DataFrame`에 모두 존재하지 않기 때문에 삭제되었습니다. SQL의 `INNER JOIN`과 동일합니다. 도시를 삭제하지 않고 `NaN`으로 채우는 `FULL OUTER JOIN`을 원하면 `how=\"outer\"`로 지정합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:29.435099Z",
     "iopub.status.busy": "2020-10-16T07:31:29.433483Z",
     "iopub.status.idle": "2020-10-16T07:31:29.439267Z",
     "shell.execute_reply": "2020-10-16T07:31:29.440577Z"
    }
   },
   "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>state_x</th>\n",
       "      <th>city</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>population</th>\n",
       "      <th>state_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>37.781334</td>\n",
       "      <td>-122.416728</td>\n",
       "      <td>808976.0</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NY</td>\n",
       "      <td>New York</td>\n",
       "      <td>40.705649</td>\n",
       "      <td>-74.008344</td>\n",
       "      <td>8363710.0</td>\n",
       "      <td>New-York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FL</td>\n",
       "      <td>Miami</td>\n",
       "      <td>25.791100</td>\n",
       "      <td>-80.320733</td>\n",
       "      <td>413201.0</td>\n",
       "      <td>Florida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OH</td>\n",
       "      <td>Cleveland</td>\n",
       "      <td>41.473508</td>\n",
       "      <td>-81.739791</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UT</td>\n",
       "      <td>Salt Lake City</td>\n",
       "      <td>40.755851</td>\n",
       "      <td>-111.896657</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Houston</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2242193.0</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state_x            city        lat         lng  population     state_y\n",
       "0      CA   San Francisco  37.781334 -122.416728    808976.0  California\n",
       "1      NY        New York  40.705649  -74.008344   8363710.0    New-York\n",
       "2      FL           Miami  25.791100  -80.320733    413201.0     Florida\n",
       "3      OH       Cleveland  41.473508  -81.739791         NaN         NaN\n",
       "4      UT  Salt Lake City  40.755851 -111.896657         NaN         NaN\n",
       "5     NaN         Houston        NaN         NaN   2242193.0       Texas"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_cities = pd.merge(left=city_loc, right=city_pop, on=\"city\", how=\"outer\")\n",
    "all_cities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "물론 `LEFT OUTER JOIN`은 `how=\"left\"`로 지정할 수 있습니다. 왼쪽의 `DataFrame`에 있는 도시만 남습니다. 비슷하게 `how=\"right\"`는 오른쪽 `DataFrame`에 있는 도시만 결과에 남습니다. 예를 들면:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:29.465273Z",
     "iopub.status.busy": "2020-10-16T07:31:29.454940Z",
     "iopub.status.idle": "2020-10-16T07:31:29.472015Z",
     "shell.execute_reply": "2020-10-16T07:31:29.470685Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>state_x</th>\n",
       "      <th>city</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>population</th>\n",
       "      <th>state_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>37.781334</td>\n",
       "      <td>-122.416728</td>\n",
       "      <td>808976</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NY</td>\n",
       "      <td>New York</td>\n",
       "      <td>40.705649</td>\n",
       "      <td>-74.008344</td>\n",
       "      <td>8363710</td>\n",
       "      <td>New-York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FL</td>\n",
       "      <td>Miami</td>\n",
       "      <td>25.791100</td>\n",
       "      <td>-80.320733</td>\n",
       "      <td>413201</td>\n",
       "      <td>Florida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Houston</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2242193</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state_x           city        lat         lng  population     state_y\n",
       "0      CA  San Francisco  37.781334 -122.416728      808976  California\n",
       "1      NY       New York  40.705649  -74.008344     8363710    New-York\n",
       "2      FL          Miami  25.791100  -80.320733      413201     Florida\n",
       "3     NaN        Houston        NaN         NaN     2242193       Texas"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left=city_loc, right=city_pop, on=\"city\", how=\"right\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "조인할 키가 `DataFrame` 인덱스라면 `left_index=True`나 `right_index=True`로 지정해야 합니다. 키 열의 이름이 다르면 `left_on`과 `right_on`을 사용합니다. 예를 들어:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:29.498246Z",
     "iopub.status.busy": "2020-10-16T07:31:29.496603Z",
     "iopub.status.idle": "2020-10-16T07:31:29.503085Z",
     "shell.execute_reply": "2020-10-16T07:31:29.504399Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
       "    .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>state_x</th>\n",
       "      <th>city</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>population</th>\n",
       "      <th>name</th>\n",
       "      <th>state_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>37.781334</td>\n",
       "      <td>-122.416728</td>\n",
       "      <td>808976</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NY</td>\n",
       "      <td>New York</td>\n",
       "      <td>40.705649</td>\n",
       "      <td>-74.008344</td>\n",
       "      <td>8363710</td>\n",
       "      <td>New York</td>\n",
       "      <td>New-York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FL</td>\n",
       "      <td>Miami</td>\n",
       "      <td>25.791100</td>\n",
       "      <td>-80.320733</td>\n",
       "      <td>413201</td>\n",
       "      <td>Miami</td>\n",
       "      <td>Florida</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state_x           city        lat         lng  population           name  \\\n",
       "0      CA  San Francisco  37.781334 -122.416728      808976  San Francisco   \n",
       "1      NY       New York  40.705649  -74.008344     8363710       New York   \n",
       "2      FL          Miami  25.791100  -80.320733      413201          Miami   \n",
       "\n",
       "      state_y  \n",
       "0  California  \n",
       "1    New-York  \n",
       "2     Florida  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_pop2 = city_pop.copy()\n",
    "city_pop2.columns = [\"population\", \"name\", \"state\"]\n",
    "pd.merge(left=city_loc, right=city_pop2, left_on=\"city\", right_on=\"name\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 연결\n",
    "\n",
    "`DataFrame`을 조인하는 대신 그냥 연결할 수도 있습니다. `concat()` 함수가 하는 일입니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:29.528602Z",
     "iopub.status.busy": "2020-10-16T07:31:29.527026Z",
     "iopub.status.idle": "2020-10-16T07:31:29.533411Z",
     "shell.execute_reply": "2020-10-16T07:31:29.532303Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>city</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>37.781334</td>\n",
       "      <td>-122.416728</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NY</td>\n",
       "      <td>New York</td>\n",
       "      <td>40.705649</td>\n",
       "      <td>-74.008344</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FL</td>\n",
       "      <td>Miami</td>\n",
       "      <td>25.791100</td>\n",
       "      <td>-80.320733</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OH</td>\n",
       "      <td>Cleveland</td>\n",
       "      <td>41.473508</td>\n",
       "      <td>-81.739791</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UT</td>\n",
       "      <td>Salt Lake City</td>\n",
       "      <td>40.755851</td>\n",
       "      <td>-111.896657</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>California</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>808976.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New-York</td>\n",
       "      <td>New York</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8363710.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Florida</td>\n",
       "      <td>Miami</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>413201.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Texas</td>\n",
       "      <td>Houston</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2242193.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        state            city        lat         lng  population\n",
       "0          CA   San Francisco  37.781334 -122.416728         NaN\n",
       "1          NY        New York  40.705649  -74.008344         NaN\n",
       "2          FL           Miami  25.791100  -80.320733         NaN\n",
       "3          OH       Cleveland  41.473508  -81.739791         NaN\n",
       "4          UT  Salt Lake City  40.755851 -111.896657         NaN\n",
       "3  California   San Francisco        NaN         NaN    808976.0\n",
       "4    New-York        New York        NaN         NaN   8363710.0\n",
       "5     Florida           Miami        NaN         NaN    413201.0\n",
       "6       Texas         Houston        NaN         NaN   2242193.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_concat = pd.concat([city_loc, city_pop])\n",
    "result_concat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 연산은 (행을 따라) 수직적으로 데이터를 연결하고 (열을 따라) 수평으로 연결하지 않습니다. 이 예에서 동일한 인덱스를 가진 행이 있습니다(예를 들면 3). 판다스는 이를 우아하게 처리합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:29.552161Z",
     "iopub.status.busy": "2020-10-16T07:31:29.550911Z",
     "iopub.status.idle": "2020-10-16T07:31:29.557417Z",
     "shell.execute_reply": "2020-10-16T07:31:29.556343Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>city</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OH</td>\n",
       "      <td>Cleveland</td>\n",
       "      <td>41.473508</td>\n",
       "      <td>-81.739791</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>California</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>808976.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        state           city        lat        lng  population\n",
       "3          OH      Cleveland  41.473508 -81.739791         NaN\n",
       "3  California  San Francisco        NaN        NaN    808976.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_concat.loc[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "또는 인덱스를 무시하도록 설정할 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:29.579980Z",
     "iopub.status.busy": "2020-10-16T07:31:29.578727Z",
     "iopub.status.idle": "2020-10-16T07:31:29.585613Z",
     "shell.execute_reply": "2020-10-16T07:31:29.584416Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>state</th>\n",
       "      <th>city</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>37.781334</td>\n",
       "      <td>-122.416728</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NY</td>\n",
       "      <td>New York</td>\n",
       "      <td>40.705649</td>\n",
       "      <td>-74.008344</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FL</td>\n",
       "      <td>Miami</td>\n",
       "      <td>25.791100</td>\n",
       "      <td>-80.320733</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OH</td>\n",
       "      <td>Cleveland</td>\n",
       "      <td>41.473508</td>\n",
       "      <td>-81.739791</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UT</td>\n",
       "      <td>Salt Lake City</td>\n",
       "      <td>40.755851</td>\n",
       "      <td>-111.896657</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>California</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>808976.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>New-York</td>\n",
       "      <td>New York</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8363710.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Florida</td>\n",
       "      <td>Miami</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>413201.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Texas</td>\n",
       "      <td>Houston</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2242193.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        state            city        lat         lng  population\n",
       "0          CA   San Francisco  37.781334 -122.416728         NaN\n",
       "1          NY        New York  40.705649  -74.008344         NaN\n",
       "2          FL           Miami  25.791100  -80.320733         NaN\n",
       "3          OH       Cleveland  41.473508  -81.739791         NaN\n",
       "4          UT  Salt Lake City  40.755851 -111.896657         NaN\n",
       "5  California   San Francisco        NaN         NaN    808976.0\n",
       "6    New-York        New York        NaN         NaN   8363710.0\n",
       "7     Florida           Miami        NaN         NaN    413201.0\n",
       "8       Texas         Houston        NaN         NaN   2242193.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([city_loc, city_pop], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "한 `DataFrame`에 열이 없을 때 `NaN`이 채워져 있는 것처럼 동작합니다. `join=\"inner\"`로 설정하면 *양쪽*의 `DataFrame`에 존재하는 열만 반환됩니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:29.604735Z",
     "iopub.status.busy": "2020-10-16T07:31:29.603386Z",
     "iopub.status.idle": "2020-10-16T07:31:29.609400Z",
     "shell.execute_reply": "2020-10-16T07:31:29.608131Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>San Francisco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NY</td>\n",
       "      <td>New York</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FL</td>\n",
       "      <td>Miami</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OH</td>\n",
       "      <td>Cleveland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UT</td>\n",
       "      <td>Salt Lake City</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>California</td>\n",
       "      <td>San Francisco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New-York</td>\n",
       "      <td>New York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Florida</td>\n",
       "      <td>Miami</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Texas</td>\n",
       "      <td>Houston</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        state            city\n",
       "0          CA   San Francisco\n",
       "1          NY        New York\n",
       "2          FL           Miami\n",
       "3          OH       Cleveland\n",
       "4          UT  Salt Lake City\n",
       "3  California   San Francisco\n",
       "4    New-York        New York\n",
       "5     Florida           Miami\n",
       "6       Texas         Houston"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([city_loc, city_pop], join=\"inner\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`axis=1`로 설정하면 `DataFrame`을 수직이 아니라 수평으로 연결할 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:29.634308Z",
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     "shell.execute_reply": "2020-10-16T07:31:29.638758Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>37.781334</td>\n",
       "      <td>-122.416728</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NY</td>\n",
       "      <td>New York</td>\n",
       "      <td>40.705649</td>\n",
       "      <td>-74.008344</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FL</td>\n",
       "      <td>Miami</td>\n",
       "      <td>25.791100</td>\n",
       "      <td>-80.320733</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OH</td>\n",
       "      <td>Cleveland</td>\n",
       "      <td>41.473508</td>\n",
       "      <td>-81.739791</td>\n",
       "      <td>808976.0</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UT</td>\n",
       "      <td>Salt Lake City</td>\n",
       "      <td>40.755851</td>\n",
       "      <td>-111.896657</td>\n",
       "      <td>8363710.0</td>\n",
       "      <td>New York</td>\n",
       "      <td>New-York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>413201.0</td>\n",
       "      <td>Miami</td>\n",
       "      <td>Florida</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>2242193.0</td>\n",
       "      <td>Houston</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state            city        lat         lng  population           city  \\\n",
       "0    CA   San Francisco  37.781334 -122.416728         NaN            NaN   \n",
       "1    NY        New York  40.705649  -74.008344         NaN            NaN   \n",
       "2    FL           Miami  25.791100  -80.320733         NaN            NaN   \n",
       "3    OH       Cleveland  41.473508  -81.739791    808976.0  San Francisco   \n",
       "4    UT  Salt Lake City  40.755851 -111.896657   8363710.0       New York   \n",
       "5   NaN             NaN        NaN         NaN    413201.0          Miami   \n",
       "6   NaN             NaN        NaN         NaN   2242193.0        Houston   \n",
       "\n",
       "        state  \n",
       "0         NaN  \n",
       "1         NaN  \n",
       "2         NaN  \n",
       "3  California  \n",
       "4    New-York  \n",
       "5     Florida  \n",
       "6       Texas  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([city_loc, city_pop], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이 경우 인덱스가 잘 정렬되지 않기 때문에 의미가 없습니다(예를 들어 Cleveland와 San Francisco의 인덱스 레이블이 `3`이기 때문에 동일한 행에 놓여 있습니다). 이 `DataFrame`을 연결하기 전에 도시로 인덱스를 재설정해 보죠:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:29.667789Z",
     "iopub.status.busy": "2020-10-16T07:31:29.648474Z",
     "iopub.status.idle": "2020-10-16T07:31:29.674949Z",
     "shell.execute_reply": "2020-10-16T07:31:29.673620Z"
    },
    "scrolled": true
   },
   "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",
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       "    }\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>state</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>population</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>San Francisco</th>\n",
       "      <td>CA</td>\n",
       "      <td>37.781334</td>\n",
       "      <td>-122.416728</td>\n",
       "      <td>808976.0</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>NY</td>\n",
       "      <td>40.705649</td>\n",
       "      <td>-74.008344</td>\n",
       "      <td>8363710.0</td>\n",
       "      <td>New-York</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miami</th>\n",
       "      <td>FL</td>\n",
       "      <td>25.791100</td>\n",
       "      <td>-80.320733</td>\n",
       "      <td>413201.0</td>\n",
       "      <td>Florida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cleveland</th>\n",
       "      <td>OH</td>\n",
       "      <td>41.473508</td>\n",
       "      <td>-81.739791</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salt Lake City</th>\n",
       "      <td>UT</td>\n",
       "      <td>40.755851</td>\n",
       "      <td>-111.896657</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Houston</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2242193.0</td>\n",
       "      <td>Texas</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               state        lat         lng  population       state\n",
       "San Francisco     CA  37.781334 -122.416728    808976.0  California\n",
       "New York          NY  40.705649  -74.008344   8363710.0    New-York\n",
       "Miami             FL  25.791100  -80.320733    413201.0     Florida\n",
       "Cleveland         OH  41.473508  -81.739791         NaN         NaN\n",
       "Salt Lake City    UT  40.755851 -111.896657         NaN         NaN\n",
       "Houston          NaN        NaN         NaN   2242193.0       Texas"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([city_loc.set_index(\"city\"), city_pop.set_index(\"city\")], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`FULL OUTER JOIN`을 수행한 것과 비슷합니다. 하지만 `state` 열이 `state_x`와 `state_y`로 바뀌지 않았고 `city` 열이 인덱스가 되었습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`append()` 메서드는 `DataFrame`을 수직으로 연결하는 단축 메서드입니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:29.698488Z",
     "iopub.status.busy": "2020-10-16T07:31:29.697097Z",
     "iopub.status.idle": "2020-10-16T07:31:29.703373Z",
     "shell.execute_reply": "2020-10-16T07:31:29.702208Z"
    }
   },
   "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",
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       "    }\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>state</th>\n",
       "      <th>city</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>37.781334</td>\n",
       "      <td>-122.416728</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NY</td>\n",
       "      <td>New York</td>\n",
       "      <td>40.705649</td>\n",
       "      <td>-74.008344</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FL</td>\n",
       "      <td>Miami</td>\n",
       "      <td>25.791100</td>\n",
       "      <td>-80.320733</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OH</td>\n",
       "      <td>Cleveland</td>\n",
       "      <td>41.473508</td>\n",
       "      <td>-81.739791</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UT</td>\n",
       "      <td>Salt Lake City</td>\n",
       "      <td>40.755851</td>\n",
       "      <td>-111.896657</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>California</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>808976.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New-York</td>\n",
       "      <td>New York</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8363710.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Florida</td>\n",
       "      <td>Miami</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>413201.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Texas</td>\n",
       "      <td>Houston</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2242193.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        state            city        lat         lng  population\n",
       "0          CA   San Francisco  37.781334 -122.416728         NaN\n",
       "1          NY        New York  40.705649  -74.008344         NaN\n",
       "2          FL           Miami  25.791100  -80.320733         NaN\n",
       "3          OH       Cleveland  41.473508  -81.739791         NaN\n",
       "4          UT  Salt Lake City  40.755851 -111.896657         NaN\n",
       "3  California   San Francisco        NaN         NaN    808976.0\n",
       "4    New-York        New York        NaN         NaN   8363710.0\n",
       "5     Florida           Miami        NaN         NaN    413201.0\n",
       "6       Texas         Houston        NaN         NaN   2242193.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_loc.append(city_pop)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "판다스의 다른 메서드와 마찬가지로 `append()` 메서드는 실제 `city_loc`을 수정하지 않습니다. 복사본을 만들어 수정한 다음 반환합니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 범주\n",
    "\n",
    "범주로 표현된 값을 가진 경우가 흔합니다. 예를 들어 `1`은 여성, `2`는 남성이거나 `\"A\"`는 좋은 것, `\"B\"`는 평균, `\"C\"`는 나쁜 것 등입니다. 범주형 값을 읽기 힘들고 다루기 번거롭습니다. 하지만 판다스에서는 간단합니다. 예를 들기 위해 앞서 만든 `city_pop` `DataFrame`에 범주를 표현하는 열을 추가해 보겠습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:29.721192Z",
     "iopub.status.busy": "2020-10-16T07:31:29.719801Z",
     "iopub.status.idle": "2020-10-16T07:31:29.725933Z",
     "shell.execute_reply": "2020-10-16T07:31:29.724844Z"
    }
   },
   "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>population</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>eco_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>808976</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>California</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8363710</td>\n",
       "      <td>New York</td>\n",
       "      <td>New-York</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>413201</td>\n",
       "      <td>Miami</td>\n",
       "      <td>Florida</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2242193</td>\n",
       "      <td>Houston</td>\n",
       "      <td>Texas</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   population           city       state  eco_code\n",
       "3      808976  San Francisco  California        17\n",
       "4     8363710       New York    New-York        17\n",
       "5      413201          Miami     Florida        34\n",
       "6     2242193        Houston       Texas        20"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_eco = city_pop.copy()\n",
    "city_eco[\"eco_code\"] = [17, 17, 34, 20]\n",
    "city_eco"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이제 `eco_code`열은 의미없는 코드입니다. 이를 바꿔 보죠. 먼저 `eco_code`를 기반으로 새로운 범주형 열을 만듭니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:29.736341Z",
     "iopub.status.busy": "2020-10-16T07:31:29.735087Z",
     "iopub.status.idle": "2020-10-16T07:31:29.740551Z",
     "shell.execute_reply": "2020-10-16T07:31:29.739607Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([17, 20, 34], dtype='int64')"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_eco[\"economy\"] = city_eco[\"eco_code\"].astype('category')\n",
    "city_eco[\"economy\"].cat.categories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "의미있는 이름을 가진 범주를 지정할 수 있습니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:29.762234Z",
     "iopub.status.busy": "2020-10-16T07:31:29.760867Z",
     "iopub.status.idle": "2020-10-16T07:31:29.767647Z",
     "shell.execute_reply": "2020-10-16T07:31:29.766196Z"
    }
   },
   "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>population</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>eco_code</th>\n",
       "      <th>economy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>808976</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>California</td>\n",
       "      <td>17</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8363710</td>\n",
       "      <td>New York</td>\n",
       "      <td>New-York</td>\n",
       "      <td>17</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>413201</td>\n",
       "      <td>Miami</td>\n",
       "      <td>Florida</td>\n",
       "      <td>34</td>\n",
       "      <td>Tourism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2242193</td>\n",
       "      <td>Houston</td>\n",
       "      <td>Texas</td>\n",
       "      <td>20</td>\n",
       "      <td>Energy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   population           city       state  eco_code  economy\n",
       "3      808976  San Francisco  California        17  Finance\n",
       "4     8363710       New York    New-York        17  Finance\n",
       "5      413201          Miami     Florida        34  Tourism\n",
       "6     2242193        Houston       Texas        20   Energy"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_eco[\"economy\"].cat.categories = [\"Finance\", \"Energy\", \"Tourism\"]\n",
    "city_eco"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "범주형 값은 알파벳 순서가 아니라 범주형 순서로 정렬합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-16T07:31:29.788100Z",
     "iopub.status.busy": "2020-10-16T07:31:29.786648Z",
     "iopub.status.idle": "2020-10-16T07:31:29.792669Z",
     "shell.execute_reply": "2020-10-16T07:31:29.791639Z"
    }
   },
   "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>population</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>eco_code</th>\n",
       "      <th>economy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>413201</td>\n",
       "      <td>Miami</td>\n",
       "      <td>Florida</td>\n",
       "      <td>34</td>\n",
       "      <td>Tourism</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2242193</td>\n",
       "      <td>Houston</td>\n",
       "      <td>Texas</td>\n",
       "      <td>20</td>\n",
       "      <td>Energy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>808976</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>California</td>\n",
       "      <td>17</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8363710</td>\n",
       "      <td>New York</td>\n",
       "      <td>New-York</td>\n",
       "      <td>17</td>\n",
       "      <td>Finance</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   population           city       state  eco_code  economy\n",
       "5      413201          Miami     Florida        34  Tourism\n",
       "6     2242193        Houston       Texas        20   Energy\n",
       "3      808976  San Francisco  California        17  Finance\n",
       "4     8363710       New York    New-York        17  Finance"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_eco.sort_values(by=\"economy\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 그 다음엔?\n",
    "\n",
    "이제 알았겠지만 판다스는 매우 커다란 라이브러리이고 기능이 많습니다. 가장 중요한 기능들을 둘러 보았지만 빙산의 일각일 뿐입니다. 더 많은 것을 익히려면 실전 데이터로 직접 실습해 보는 것이 제일 좋습니다. 판다스의 훌륭한 [문서](http://pandas.pydata.org/pandas-docs/stable/index.html)와 [쿡북](http://pandas.pydata.org/pandas-docs/stable/cookbook.html)을 보는 것도 좋습니다."
   ]
  }
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