Day10Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "3    4\n",
       "4    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ls = [1,2,3,4,5]\n",
    "ts = pd.Series(ls)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "d    4\n",
       "e    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = pd.Series(ls, index=['a', 'b', 'c', 'd', 'e'])\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "--------------------\n",
      "a     1\n",
      "b     2\n",
      "c     3\n",
      "d     4\n",
      "e    10\n",
      "dtype: int64\n",
      "--------------------\n",
      "d     4\n",
      "e    10\n",
      "dtype: int64\n",
      "--------------------\n",
      "a     2\n",
      "b     4\n",
      "c     6\n",
      "d     8\n",
      "e    20\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# indexing\n",
    "print(ts['b'])\n",
    "print('-'*20)\n",
    "\n",
    "# indexing으로 값 설정\n",
    "ts['e'] = 10\n",
    "print(ts)\n",
    "print('-'*20)\n",
    "\n",
    "# boolean indexing\n",
    "print(ts[ts>3])\n",
    "print('-'*20)\n",
    "\n",
    "# broad casting\n",
    "print(ts*2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['a', 'b', 'c', 'd', 'e'], dtype='object')\n",
      "[ 1  2  3  4 10]\n",
      "가     1\n",
      "나     2\n",
      "다     3\n",
      "라     4\n",
      "마    10\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(ts.index)\n",
    "print(ts.values)\n",
    "# index재설정하기\n",
    "ts.index = ['가', '나', '다', '라', '마']\n",
    "print(ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>pretestscore</th>\n",
       "      <th>posttestscore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Jason</td>\n",
       "      <td>miller</td>\n",
       "      <td>42.0</td>\n",
       "      <td>m</td>\n",
       "      <td>4.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>tina</td>\n",
       "      <td>ali</td>\n",
       "      <td>36.0</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>jake</td>\n",
       "      <td>miler</td>\n",
       "      <td>24.0</td>\n",
       "      <td>m</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>amy</td>\n",
       "      <td>coozer</td>\n",
       "      <td>73.0</td>\n",
       "      <td>f</td>\n",
       "      <td>3.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  first_name last_name   age  sex  pretestscore  posttestscore\n",
       "0      Jason    miller  42.0    m           4.0           25.0\n",
       "1        NaN       NaN   NaN  NaN           NaN            NaN\n",
       "2       tina       ali  36.0    f           NaN            NaN\n",
       "3       jake     miler  24.0    m           2.0           62.0\n",
       "4        amy    coozer  73.0    f           3.0           70.0"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = {'first_name': ['Jason', np.nan, 'tina', 'jake', 'amy'],\n",
    "           'last_name':['miller', np.nan, 'ali', 'miler', 'coozer'],\n",
    "           'age': [42, np.nan, 36, 24, 73],\n",
    "           'sex':['m', np.nan, 'f', 'm', 'f'],\n",
    "           'pretestscore':[4,np.nan,np.nan,2,3],\n",
    "           'posttestscore':[25, np.nan, np.nan, 62, 70]}\n",
    "\n",
    "df = pd.DataFrame(raw_data)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Jason</td>\n",
       "      <td>miller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>tina</td>\n",
       "      <td>ali</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>jake</td>\n",
       "      <td>miler</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>amy</td>\n",
       "      <td>coozer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  first_name last_name\n",
       "0      Jason    miller\n",
       "1        NaN       NaN\n",
       "2       tina       ali\n",
       "3       jake     miler\n",
       "4        amy    coozer"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame(raw_data, columns=['first_name', 'last_name'])\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['first_name', 'last_name', 'age', 'sex', 'pretestscore',\n",
      "       'posttestscore'],\n",
      "      dtype='object')\n",
      "[0, 1, 2, 3, 4]\n"
     ]
    },
    {
     "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>fn</th>\n",
       "      <th>ln</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Jason</td>\n",
       "      <td>miller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>tina</td>\n",
       "      <td>ali</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>jake</td>\n",
       "      <td>miler</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>amy</td>\n",
       "      <td>coozer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      fn      ln\n",
       "0  Jason  miller\n",
       "1    NaN     NaN\n",
       "2   tina     ali\n",
       "3   jake   miler\n",
       "4    amy  coozer"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df.columns)\n",
    "print(list(df.index))\n",
    "df1.columns = ['fn', 'ln']\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    42.0\n",
      "1     NaN\n",
      "2    36.0\n",
      "3    24.0\n",
      "4    73.0\n",
      "Name: age, dtype: float64\n",
      "0    miller\n",
      "1       NaN\n",
      "2       ali\n",
      "3     miler\n",
      "4    coozer\n",
      "Name: last_name, dtype: object\n",
      "  first_name last_name\n",
      "0      Jason    miller\n",
      "1        NaN       NaN\n",
      "2       tina       ali\n",
      "3       jake     miler\n",
      "4        amy    coozer\n"
     ]
    }
   ],
   "source": [
    "print(df['age'])\n",
    "print(df['last_name'])\n",
    "print(df[['first_name','last_name']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      m\n",
      "1    NaN\n",
      "2      f\n",
      "3      m\n",
      "4      f\n",
      "Name: sex, dtype: object\n",
      "0    Jason\n",
      "1      NaN\n",
      "2     tina\n",
      "3     jake\n",
      "4      amy\n",
      "Name: first_name, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# dataframe의 인덱스를 이용하는 방법과 속성을 이용하는 방법\n",
    "print(df['sex'])\n",
    "\n",
    "# 속성 사용의 제약사앙: 삽입, 공백있는 컬럼명\n",
    "print(df.first_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first_name        Jason\n",
      "last_name        miller\n",
      "age                  42\n",
      "sex                   m\n",
      "pretestscore          4\n",
      "posttestscore        25\n",
      "Name: a, dtype: object\n",
      "-------------------------\n",
      "first_name        Jason\n",
      "last_name        miller\n",
      "age                  42\n",
      "sex                   m\n",
      "pretestscore          4\n",
      "posttestscore        25\n",
      "Name: a, dtype: object\n",
      "-------------------------\n",
      "first_name          amy\n",
      "last_name        coozer\n",
      "age                  73\n",
      "sex                   f\n",
      "pretestscore          3\n",
      "posttestscore        70\n",
      "Name: e, dtype: object\n",
      "-------------------------\n",
      "  first_name last_name   age  sex  pretestscore  posttestscore\n",
      "e        amy    coozer  73.0    f           3.0           70.0\n",
      "b        NaN       NaN   NaN  NaN           NaN            NaN\n",
      "-------------------------\n",
      "miller\n",
      "-------------------------\n",
      "last_name     miller\n",
      "first_name     Jason\n",
      "Name: a, dtype: object\n",
      "-------------------------\n",
      "  first_name last_name   age  sex  pretestscore  posttestscore\n",
      "a      Jason    miller  42.0    m           4.0           25.0\n",
      "b        NaN       NaN   NaN  NaN           NaN            NaN\n",
      "-------------------------\n",
      "  last_name   age  sex\n",
      "a    miller  42.0    m\n",
      "b       NaN   NaN  NaN\n",
      "c       ali  36.0    f\n",
      "-------------------------\n",
      "  last_name   age  sex\n",
      "a    miller  42.0    m\n",
      "b       NaN   NaN  NaN\n",
      "c       ali  36.0    f\n",
      "-------------------------\n",
      "<class 'pandas.core.series.Series'>\n",
      "<class 'pandas.core.series.Series'>\n",
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "df.index = ['a', 'b', 'c', 'd', 'e']\n",
    "\n",
    "# row접근하기\n",
    "print(df.loc['a']) #Series타입\n",
    "print('-'*25)\n",
    "print(df.iloc[0])\n",
    "print('-'*25)\n",
    "print(df.iloc[-1])\n",
    "print('-'*25)\n",
    "print(df.iloc[[-1,1]])\n",
    "print('-'*25)\n",
    "print(df.loc['a', 'last_name'])\n",
    "print('-'*25)\n",
    "print(df.loc['a', ['last_name', 'first_name']])\n",
    "print('-'*25)\n",
    "print(df.iloc[[0,1]])\n",
    "print('-'*25)\n",
    "print(df.loc['a':'c','last_name':'sex']) # 끝 인덱스 포함\n",
    "print('-'*25)\n",
    "print(df.iloc[0:3,1:4]) #끝인덱스 제외(파이썬 일반 문법과 같다)\n",
    "print('-'*25)\n",
    "# 타입은 시리즈\n",
    "print(type(df.loc['a']))\n",
    "print(type(df.iloc[-1]))\n",
    "# 둘 이상의 row를 가져올 때의 타입은 DataFrame\n",
    "print(type(df.loc[['a', 'd']]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  sex  pretestscore  posttestscore\n",
      "c   f           NaN            NaN\n",
      "d   m           2.0           62.0\n",
      "e   f           3.0           70.0\n",
      "  sex  pretestscore  posttestscore\n",
      "c   f           NaN            NaN\n",
      "d   m           2.0           62.0\n",
      "e   f           3.0           70.0\n",
      "  last_name  pretestscore\n",
      "c       ali           NaN\n",
      "d     miler           2.0\n",
      "  last_name  pretestscore\n",
      "c       ali           NaN\n",
      "d     miler           2.0\n"
     ]
    }
   ],
   "source": [
    "# df에서 row는 'c'~'e', column은 'sex'~'posttestscore'를 출력하기\n",
    "print(df.loc['c':, 'sex':])\n",
    "print(df.iloc[2:, 3:])\n",
    "# ali, miler, nan, 2.0을 출력하기\n",
    "print(df.iloc[2:4, [1,4]])\n",
    "print(df.iloc[2:4, 1::3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  first_name last_name   age sex  pretestscore  posttestscore\n",
      "a      Jason    miller  42.0   m           4.0           25.0\n",
      "e        amy    coozer  73.0   f           3.0           70.0\n",
      "  first_name last_name   age sex  pretestscore  posttestscore\n",
      "a      Jason    miller  42.0   m           4.0           25.0\n",
      "e        amy    coozer  73.0   f           3.0           70.0\n",
      "['miller', 'coozer']\n"
     ]
    }
   ],
   "source": [
    "# age가 40이상인 데이터를 추출하기\n",
    "print(df[df.age>40])\n",
    "print(df[df['age']>40])\n",
    "print(df[df.age>40]['last_name'].values.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a    4.0\n",
      "Name: pretestscore, dtype: float64\n",
      "a    4.0\n",
      "Name: pretestscore, dtype: float64\n",
      "[4.]\n"
     ]
    }
   ],
   "source": [
    "### jason의 pretestscore는 얼마인가\n",
    "print(df[df.first_name=='Jason']['pretestscore'])\n",
    "print(df[df['first_name']=='Jason']['pretestscore'])\n",
    "print(df[df.first_name=='Jason']['pretestscore'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54.5\n",
      "54.5\n",
      "54.5\n"
     ]
    }
   ],
   "source": [
    "### 여성의 평균나이\n",
    "print(np.mean(df[df.sex=='f'].age))\n",
    "print(np.mean(df[df['sex']=='f']['age']))\n",
    "# pandas데이터는 numpy함수를 바로 사용할 수 있다\n",
    "print(df[df.sex=='f']['age'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  first_name last_name\n",
      "e        amy    coozer\n",
      "e    amy\n",
      "Name: first_name, dtype: object\n"
     ]
    }
   ],
   "source": [
    "### 최고령자의 이름 출력\n",
    "print(df[df['age']==max(df.age)][['first_name','last_name']])\n",
    "print(df[df.age==max(df.age)].first_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Column추가"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['addr']=['Seoul', 'Incheon', 'Gwangju', 'Pusan', 'Seoul']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['age']=[42,25,78,41,23]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.iloc[1,3] = 'f'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>o</th>\n",
       "      <th>t</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "      <th>five</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>a</td>\n",
       "      <td>0.666291</td>\n",
       "      <td>0.944198</td>\n",
       "      <td>0.691329</td>\n",
       "      <td>0.724165</td>\n",
       "      <td>0.224716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>b</td>\n",
       "      <td>0.722328</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.076171</td>\n",
       "      <td>0.681876</td>\n",
       "      <td>0.732880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>c</td>\n",
       "      <td>0.167351</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.292315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>d</td>\n",
       "      <td>0.026953</td>\n",
       "      <td>0.293724</td>\n",
       "      <td>0.665255</td>\n",
       "      <td>0.337151</td>\n",
       "      <td>0.615927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>e</td>\n",
       "      <td>0.582858</td>\n",
       "      <td>0.107225</td>\n",
       "      <td>0.113393</td>\n",
       "      <td>0.576195</td>\n",
       "      <td>0.169406</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          o         t     three      four      five\n",
       "a  0.666291  0.944198  0.691329  0.724165  0.224716\n",
       "b  0.722328       NaN  0.076171  0.681876  0.732880\n",
       "c  0.167351       NaN       NaN       NaN  0.292315\n",
       "d  0.026953  0.293724  0.665255  0.337151  0.615927\n",
       "e  0.582858  0.107225  0.113393  0.576195  0.169406"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# column명 변경 함수 rename\n",
    "df.rename(columns={'one':'o','two':'t'}, inplace=True)\n",
    "#return해줌\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Length mismatch: Expected axis has 5 elements, new values have 7 elements",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-236-2a4ae482da5a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#columns를 이용한 column명 변경 즉각적으로 값이 변경된다\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'fn'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'ln'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'age'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'sex'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'pre'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'post'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'addr'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__setattr__\u001b[1;34m(self, name, value)\u001b[0m\n\u001b[0;32m   5190\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5191\u001b[0m             \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5192\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5193\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5194\u001b[0m             \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\properties.pyx\u001b[0m in \u001b[0;36mpandas._libs.properties.AxisProperty.__set__\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_set_axis\u001b[1;34m(self, axis, labels)\u001b[0m\n\u001b[0;32m    688\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    689\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_set_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 690\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    691\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_clear_item_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    692\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mset_axis\u001b[1;34m(self, axis, new_labels)\u001b[0m\n\u001b[0;32m    181\u001b[0m             raise ValueError(\n\u001b[0;32m    182\u001b[0m                 \u001b[1;34m\"Length mismatch: Expected axis has {old} elements, new \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 183\u001b[1;33m                 \u001b[1;34m\"values have {new} elements\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mold\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mold_len\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnew\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnew_len\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    184\u001b[0m             )\n\u001b[0;32m    185\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Length mismatch: Expected axis has 5 elements, new values have 7 elements"
     ]
    }
   ],
   "source": [
    "#columns를 이용한 column명 변경 즉각적으로 값이 변경된다\n",
    "df.columns = ['fn','ln','age','sex','pre','post','addr']\n",
    "df\n",
    "# rename과 비교\n",
    "#df.rename(columns={'first_name': 'fn', 'last_name': 'ln'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe 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>fn</th>\n",
       "      <th>ln</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>pre</th>\n",
       "      <th>post</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>a</td>\n",
       "      <td>Jason</td>\n",
       "      <td>miller</td>\n",
       "      <td>42</td>\n",
       "      <td>m</td>\n",
       "      <td>4.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>b</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>c</td>\n",
       "      <td>tina</td>\n",
       "      <td>ali</td>\n",
       "      <td>78</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>d</td>\n",
       "      <td>jake</td>\n",
       "      <td>miler</td>\n",
       "      <td>41</td>\n",
       "      <td>m</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>e</td>\n",
       "      <td>amy</td>\n",
       "      <td>coozer</td>\n",
       "      <td>23</td>\n",
       "      <td>f</td>\n",
       "      <td>3.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      fn      ln  age sex  pre  post\n",
       "a  Jason  miller   42   m  4.0  25.0\n",
       "b    NaN     NaN   25   f  NaN   NaN\n",
       "c   tina     ali   78   f  NaN   NaN\n",
       "d   jake   miler   41   m  2.0  62.0\n",
       "e    amy  coozer   23   f  3.0  70.0"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('addr', axis=1, inplace=True)\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\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>fn</th>\n",
       "      <th>ln</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>pre</th>\n",
       "      <th>post</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>a</td>\n",
       "      <td>Jason</td>\n",
       "      <td>miller</td>\n",
       "      <td>42</td>\n",
       "      <td>m</td>\n",
       "      <td>4.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>c</td>\n",
       "      <td>tina</td>\n",
       "      <td>ali</td>\n",
       "      <td>78</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>d</td>\n",
       "      <td>jake</td>\n",
       "      <td>miler</td>\n",
       "      <td>41</td>\n",
       "      <td>m</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>e</td>\n",
       "      <td>amy</td>\n",
       "      <td>coozer</td>\n",
       "      <td>23</td>\n",
       "      <td>f</td>\n",
       "      <td>3.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      fn      ln  age sex  pre  post\n",
       "a  Jason  miller   42   m  4.0  25.0\n",
       "c   tina     ali   78   f  NaN   NaN\n",
       "d   jake   miler   41   m  2.0  62.0\n",
       "e    amy  coozer   23   f  3.0  70.0"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('b', inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fn</th>\n",
       "      <th>ln</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Jason</td>\n",
       "      <td>miller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>tina</td>\n",
       "      <td>ali</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>jake</td>\n",
       "      <td>miler</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>amy</td>\n",
       "      <td>coozer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      fn      ln\n",
       "0  Jason  miller\n",
       "1    NaN     NaN\n",
       "2   tina     ali\n",
       "3   jake   miler\n",
       "4    amy  coozer"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " C 드라이브의 볼륨에는 이름이 없습니다.\n",
      " 볼륨 일련 번호: 7096-B02E\n",
      "\n",
      " C:\\04Python 디렉터리\n",
      "\n",
      "2020-01-21  오후 04:16    <DIR>          .\n",
      "2020-01-21  오후 04:16    <DIR>          ..\n",
      "2020-01-21  오후 02:15    <DIR>          .ipynb_checkpoints\n",
      "2020-01-16  오후 04:08            50,406 access_log\n",
      "2020-01-16  오전 09:35         2,317,454 age.csv\n",
      "2020-01-10  오후 03:04                22 ban.txt\n",
      "2020-01-10  오후 03:04                93 ban_student.txt\n",
      "2020-01-15  오후 02:05             1,871 birth_by_years.csv\n",
      "2020-01-15  오후 02:20             3,969 birth_by_years1.csv\n",
      "2020-01-15  오후 03:16    <DIR>          CSV\n",
      "2020-01-15  오후 03:16            18,944 csv_files.selection.tar\n",
      "2020-01-17  오전 09:14                32 data.pickle\n",
      "2020-01-14  오후 03:19        24,307,200 data.selection.tar\n",
      "2020-01-16  오후 03:18            18,231 Day01BasicDataform.ipynb\n",
      "2020-01-16  오후 03:18            11,981 Day01List.ipynb\n",
      "2020-01-08  오후 05:36            10,867 Day01Summary.ipynb\n",
      "2020-01-16  오후 03:19             7,667 Day01summary2.ipynb\n",
      "2020-01-16  오후 03:18             8,667 Day01tuple_set_dict.ipynb\n",
      "2020-01-16  오후 03:17            19,955 Day02if_for_while.ipynb\n",
      "2020-01-10  오후 05:40             7,301 Day03Class.ipynb\n",
      "2020-01-13  오전 09:06            97,935 Day03file.ipynb\n",
      "2020-01-16  오후 03:17            10,106 Day03function.ipynb\n",
      "2020-01-14  오후 02:31            23,452 Day04 RegularExpression.ipynb\n",
      "2020-01-13  오후 05:26         8,498,099 Day04CSV.ipynb\n",
      "2020-01-13  오전 10:37            15,991 Day04Exception.ipynb\n",
      "2020-01-15  오후 05:54           266,903 Day04matplotlib.ipynb\n",
      "2020-01-16  오후 05:50         2,219,827 Day05FileReview.ipynb\n",
      "2020-01-15  오후 04:56            88,123 Day05File_Directory.ipynb\n",
      "2020-01-16  오후 05:50            22,452 Day05PythonReview.ipynb\n",
      "2020-01-17  오후 04:02             6,136 Day05RegExReview.ipynb\n",
      "2020-01-16  오후 03:15            13,962 Day06ClassReviews.ipynb\n",
      "2020-01-17  오후 04:02            46,014 Day06NumpyMatpolib.ipynb\n",
      "2020-01-15  오후 04:56             7,009 Day06Quiz.ipynb\n",
      "2020-01-16  오후 05:50           235,820 Day07CSVandMatplotlib.ipynb\n",
      "2020-01-16  오후 03:15               640 Day07LambdaReview.ipynb\n",
      "2020-01-16  오후 12:02               820 Day07ModuleReview.ipynb\n",
      "2020-01-17  오후 04:02             7,061 Day08Pickle.ipynb\n",
      "2020-01-20  오전 09:30            25,942 Day08PythonReview.ipynb\n",
      "2020-01-20  오전 09:25            10,996 Day08Test(blank).ipynb\n",
      "2020-01-20  오후 02:24            21,523 Day09ErrorExamples.ipynb\n",
      "2020-01-20  오후 05:49            29,093 Day09Numpy-Copy1.ipynb\n",
      "2020-01-20  오후 04:54            25,711 Day09Numpy.ipynb\n",
      "2020-01-20  오후 12:38            10,748 Day09PythonReview2.ipynb\n",
      "2020-01-21  오후 04:16            25,620 Day10Pandas.ipynb\n",
      "2020-01-10  오후 03:04             5,120 debug.log\n",
      "2020-01-16  오후 03:16         2,080,612 Exercise.ipynb\n",
      "2020-01-13  오전 10:35                11 file\n",
      "2020-01-16  오전 11:52            10,983 function_code_lec_100110.ipynb\n",
      "2020-01-16  오전 10:44         4,221,195 gender.csv\n",
      "2020-01-10  오후 04:02             2,175 googoo.jpg\n",
      "2020-01-17  오후 04:36               833 guguclass.ipynb\n",
      "2020-01-09  오후 05:44             2,554 Laboratory.ipynb\n",
      "2020-01-16  오전 11:42           365,928 matplotlib_lec_code_100113.ipynb\n",
      "2020-01-13  오후 02:44            18,190 matpolib.jpg\n",
      "2020-01-16  오후 04:41               146 module.py\n",
      "2020-01-16  오전 11:52             3,058 module_test_200116.ipynb\n",
      "2020-01-15  오후 04:15             1,113 mydata.txt\n",
      "2020-01-17  오후 05:18                32 mytest.pickle\n",
      "2020-01-17  오후 05:18                81 mytest.txt\n",
      "2020-01-10  오후 02:33                29 my_file.txt\n",
      "2020-01-14  오후 04:30    <DIR>          names\n",
      "2020-01-13  오전 09:13                10 new.txt\n",
      "2020-01-20  오전 08:43                32 pic.pickle\n",
      "2020-01-15  오후 03:39               240 Pname.txt\n",
      "2020-01-10  오후 02:37               395 poem.txt\n",
      "2020-01-10  오후 04:01                54 python.txt\n",
      "2020-01-13  오후 02:48         1,176,335 seoul.csv\n",
      "2020-01-10  오후 03:04            16,339 stockcode.txt\n",
      "2020-01-16  오후 05:43            32,434 subwayfee.csv\n",
      "2020-01-16  오후 12:04           156,880 subwaytime.csv\n",
      "2020-01-21  오후 04:22               130 test.csv\n",
      "2020-01-16  오전 09:27               220 test.py\n",
      "2020-01-10  오후 02:26                50 test.txt\n",
      "2020-01-14  오후 03:45    <DIR>          testdir\n",
      "2020-01-14  오후 02:19                65 test_file.txt\n",
      "2020-01-16  오전 11:53               924 test_module.py\n",
      "2020-01-10  오후 03:48                40 text.txt\n",
      "2020-01-10  오전 10:10             7,948 Untitled.ipynb\n",
      "2020-01-14  오후 04:04               555 Untitled1.ipynb\n",
      "2020-01-17  오전 08:42                72 Untitled2.ipynb\n",
      "2020-01-21  오전 09:00                72 Untitled3.ipynb\n",
      "2020-01-21  오전 09:03               555 Untitled4.ipynb\n",
      "2020-01-21  오후 02:02                72 Untitled5.ipynb\n",
      "2020-01-16  오전 09:31    <DIR>          __pycache__\n",
      "2020-01-10  오후 04:06                 0 비둘기.jpg\n",
      "2020-01-10  오후 04:09             2,175 비둘기1.jpg\n",
      "2020-01-10  오후 02:25                 0 새 텍스트 문서.txt\n",
      "2020-01-10  오후 05:19    <DIR>          새 폴더\n",
      "              80개 파일          46,602,300 바이트\n",
      "               8개 디렉터리  48,584,544,256 바이트 남음\n"
     ]
    }
   ],
   "source": [
    "!dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\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>fn</th>\n",
       "      <th>ln</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>pre</th>\n",
       "      <th>post</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>a</td>\n",
       "      <td>Jason</td>\n",
       "      <td>miller</td>\n",
       "      <td>42</td>\n",
       "      <td>m</td>\n",
       "      <td>4.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>c</td>\n",
       "      <td>tina</td>\n",
       "      <td>ali</td>\n",
       "      <td>78</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>d</td>\n",
       "      <td>jake</td>\n",
       "      <td>miler</td>\n",
       "      <td>41</td>\n",
       "      <td>m</td>\n",
       "      <td>2.0</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>e</td>\n",
       "      <td>amy</td>\n",
       "      <td>coozer</td>\n",
       "      <td>23</td>\n",
       "      <td>f</td>\n",
       "      <td>3.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      fn      ln  age sex  pre  post\n",
       "a  Jason  miller   42   m  4.0  25.0\n",
       "c   tina     ali   78   f  NaN   NaN\n",
       "d   jake   miler   41   m  2.0  62.0\n",
       "e    amy  coozer   23   f  3.0  70.0"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fd = pd.read_csv('test.csv',index_col=0)\n",
    "fd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 결측치 처리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "      <th>five</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>a</td>\n",
       "      <td>0.666291</td>\n",
       "      <td>0.944198</td>\n",
       "      <td>0.691329</td>\n",
       "      <td>0.724165</td>\n",
       "      <td>0.224716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>b</td>\n",
       "      <td>0.722328</td>\n",
       "      <td>0.889167</td>\n",
       "      <td>0.076171</td>\n",
       "      <td>0.681876</td>\n",
       "      <td>0.732880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>c</td>\n",
       "      <td>0.167351</td>\n",
       "      <td>0.297014</td>\n",
       "      <td>0.002930</td>\n",
       "      <td>0.993480</td>\n",
       "      <td>0.292315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>d</td>\n",
       "      <td>0.026953</td>\n",
       "      <td>0.293724</td>\n",
       "      <td>0.665255</td>\n",
       "      <td>0.337151</td>\n",
       "      <td>0.615927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>e</td>\n",
       "      <td>0.582858</td>\n",
       "      <td>0.107225</td>\n",
       "      <td>0.113393</td>\n",
       "      <td>0.576195</td>\n",
       "      <td>0.169406</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        one       two     three      four      five\n",
       "a  0.666291  0.944198  0.691329  0.724165  0.224716\n",
       "b  0.722328  0.889167  0.076171  0.681876  0.732880\n",
       "c  0.167351  0.297014  0.002930  0.993480  0.292315\n",
       "d  0.026953  0.293724  0.665255  0.337151  0.615927\n",
       "e  0.582858  0.107225  0.113393  0.576195  0.169406"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# null값 처리 (삭제/대체)\n",
    "\n",
    "# df만들기\n",
    "df = pd.DataFrame(np.random.rand(5,5), columns = ['one', 'two', 'three', 'four', 'five'], index=['a', 'b', 'c','d','e'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "      <th>five</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>a</td>\n",
       "      <td>0.666291</td>\n",
       "      <td>0.944198</td>\n",
       "      <td>0.691329</td>\n",
       "      <td>0.724165</td>\n",
       "      <td>0.224716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>b</td>\n",
       "      <td>0.722328</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.076171</td>\n",
       "      <td>0.681876</td>\n",
       "      <td>0.732880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>c</td>\n",
       "      <td>0.167351</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.292315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>d</td>\n",
       "      <td>0.026953</td>\n",
       "      <td>0.293724</td>\n",
       "      <td>0.665255</td>\n",
       "      <td>0.337151</td>\n",
       "      <td>0.615927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>e</td>\n",
       "      <td>0.582858</td>\n",
       "      <td>0.107225</td>\n",
       "      <td>0.113393</td>\n",
       "      <td>0.576195</td>\n",
       "      <td>0.169406</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        one       two     three      four      five\n",
       "a  0.666291  0.944198  0.691329  0.724165  0.224716\n",
       "b  0.722328       NaN  0.076171  0.681876  0.732880\n",
       "c  0.167351       NaN       NaN       NaN  0.292315\n",
       "d  0.026953  0.293724  0.665255  0.337151  0.615927\n",
       "e  0.582858  0.107225  0.113393  0.576195  0.169406"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#nan값으로 덮기\n",
    "df.iloc[1:3, 1] = np.nan\n",
    "df.iloc[2, 2:4] = np.nan\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "      <th>five</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>a</td>\n",
       "      <td>0.666291</td>\n",
       "      <td>0.944198</td>\n",
       "      <td>0.691329</td>\n",
       "      <td>0.724165</td>\n",
       "      <td>0.224716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>b</td>\n",
       "      <td>0.722328</td>\n",
       "      <td>0.448382</td>\n",
       "      <td>0.076171</td>\n",
       "      <td>0.681876</td>\n",
       "      <td>0.732880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>c</td>\n",
       "      <td>0.167351</td>\n",
       "      <td>0.448382</td>\n",
       "      <td>0.386537</td>\n",
       "      <td>0.579847</td>\n",
       "      <td>0.292315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>d</td>\n",
       "      <td>0.026953</td>\n",
       "      <td>0.293724</td>\n",
       "      <td>0.665255</td>\n",
       "      <td>0.337151</td>\n",
       "      <td>0.615927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>e</td>\n",
       "      <td>0.582858</td>\n",
       "      <td>0.107225</td>\n",
       "      <td>0.113393</td>\n",
       "      <td>0.576195</td>\n",
       "      <td>0.169406</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        one       two     three      four      five\n",
       "a  0.666291  0.944198  0.691329  0.724165  0.224716\n",
       "b  0.722328  0.448382  0.076171  0.681876  0.732880\n",
       "c  0.167351  0.448382  0.386537  0.579847  0.292315\n",
       "d  0.026953  0.293724  0.665255  0.337151  0.615927\n",
       "e  0.582858  0.107225  0.113393  0.576195  0.169406"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull()\n",
    "#df.isnull().sum(axis=0) column기준\n",
    "#df.isnull().sum(axis=1) row기준\n",
    "# 1. null값 있느 행이나 컬럼을 삭제하기 dropna()\n",
    "df.dropna()#axis=0 row기준/ axis=1 column기준\n",
    "df.dropna(how='all') #전부 nan인 데이터 삭제\n",
    "df.dropna(how='any') # 하나라도 nan인 데이터 삭제\n",
    "df.dropna(thresh=3) # 3개 미만의 nan이 아닌 데이터를 가지고 있으면 삭제\n",
    "\n",
    "\n",
    "# 2. null값을 다른 값으로 대체하기 fillna()\n",
    "df.fillna(0)\n",
    "df.fillna(0.3)\n",
    "# 바로 앞값으로 채워줌\n",
    "df.fillna(method='ffill')\n",
    "# 바로 뒷값으로 채워줌\n",
    "df.fillna(method='bfill')\n",
    "df.fillna(df.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Advanced Pandas\n",
    "### GroupBy\n",
    "\n",
    "\n",
    "참고     \n",
    "corr()   \n",
    "srt.contains()   \n",
    "value_counts()   \n",
    "sort_values()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Team': ['Riders',\n",
       "  'Riders',\n",
       "  'Devils',\n",
       "  'Devils',\n",
       "  'Kings',\n",
       "  'Giants',\n",
       "  'Kings',\n",
       "  'Kings',\n",
       "  'Riders',\n",
       "  'Royals',\n",
       "  'Royals',\n",
       "  'Riders'],\n",
       " 'Rank': [1, 2, 2, 3, 3, 4, 1, 1, 2, 4, 1, 2],\n",
       " 'Year': [2014,\n",
       "  2015,\n",
       "  2014,\n",
       "  2015,\n",
       "  2014,\n",
       "  2015,\n",
       "  2016,\n",
       "  2017,\n",
       "  2016,\n",
       "  2014,\n",
       "  2015,\n",
       "  2017],\n",
       " 'Points': [876, 789, 863, 673, 741, 812, 756, 788, 694, 701, 804, 690]}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "data = {'Team': ['Riders', 'Riders', 'Devils', 'Devils', 'Kings', 'Giants', 'Kings', 'Kings', 'Riders', \n",
    "                 'Royals', 'Royals', 'Riders'],\n",
    "       'Rank':[1,2,2,3,3,4,1,1,2,4,1,2],\n",
    "       'Year':[2014,2015,2014,2015,2014,2015,2016,2017,2016,2014,2015,2017],\n",
    "       'Points':[876,789,863,673,741,812,756,788,694,701,804,690]}\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n",
      "12\n",
      "12\n",
      "12\n"
     ]
    }
   ],
   "source": [
    "# 데이터 각 column 크기 확인\n",
    "for i in data:\n",
    "    print(len(data[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>Rank</th>\n",
       "      <th>Year</th>\n",
       "      <th>Points</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Riders</td>\n",
       "      <td>1</td>\n",
       "      <td>2014</td>\n",
       "      <td>876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Riders</td>\n",
       "      <td>2</td>\n",
       "      <td>2015</td>\n",
       "      <td>789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Devils</td>\n",
       "      <td>2</td>\n",
       "      <td>2014</td>\n",
       "      <td>863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Devils</td>\n",
       "      <td>3</td>\n",
       "      <td>2015</td>\n",
       "      <td>673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Kings</td>\n",
       "      <td>3</td>\n",
       "      <td>2014</td>\n",
       "      <td>741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Giants</td>\n",
       "      <td>4</td>\n",
       "      <td>2015</td>\n",
       "      <td>812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Kings</td>\n",
       "      <td>1</td>\n",
       "      <td>2016</td>\n",
       "      <td>756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Kings</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Riders</td>\n",
       "      <td>2</td>\n",
       "      <td>2016</td>\n",
       "      <td>694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Royals</td>\n",
       "      <td>4</td>\n",
       "      <td>2014</td>\n",
       "      <td>701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>Royals</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Riders</td>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>690</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Team  Rank  Year  Points\n",
       "0   Riders     1  2014     876\n",
       "1   Riders     2  2015     789\n",
       "2   Devils     2  2014     863\n",
       "3   Devils     3  2015     673\n",
       "4    Kings     3  2014     741\n",
       "5   Giants     4  2015     812\n",
       "6    Kings     1  2016     756\n",
       "7    Kings     1  2017     788\n",
       "8   Riders     2  2016     694\n",
       "9   Royals     4  2014     701\n",
       "10  Royals     1  2015     804\n",
       "11  Riders     2  2017     690"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(data)\n",
    "df \n",
    "# 같은 팀의 데이터끼리 비교"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kings 761.6666666666666\n",
      "Riders 762.25\n",
      "Devils 768.0\n",
      "Giants 812.0\n",
      "Royals 752.5\n",
      "768.0\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "# GroupBy를 안쓰고 팀끼리 계산하기\n",
    "S = set(df.Team)\n",
    "for t in S:\n",
    "    print(t,df[df.Team==t].Points.mean())\n",
    "\n",
    "print(df[df.Team=='Devils'].Points.mean())\n",
    "print(df[df.Team=='Devils'].Points.count())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GroupBy => 동일한 데이터끼리 데이터를 묶는다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Team\n",
       "Devils    768.000000\n",
       "Giants    812.000000\n",
       "Kings     761.666667\n",
       "Riders    762.250000\n",
       "Royals    752.500000\n",
       "Name: Points, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Team')['Points'].mean()\n",
    "df.groupby('Team').Points.mean() #둘다가능"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Team  Rank  Year  Points\n",
      "5  Giants     4  2015     812\n",
      "      Team  Rank  Year  Points\n",
      "0   Riders     1  2014     876\n",
      "1   Riders     2  2015     789\n",
      "8   Riders     2  2016     694\n",
      "11  Riders     2  2017     690\n",
      "            Points  Year\n",
      "Team                    \n",
      "Devils  768.000000  4029\n",
      "Giants  812.000000  2015\n",
      "Kings   761.666667  6047\n",
      "Riders  762.250000  8062\n",
      "Royals  752.500000  4029\n",
      "       Rank\n",
      "Team       \n",
      "Devils   2등\n",
      "Giants   1등\n",
      "Kings    3등\n",
      "Riders   4등\n",
      "Royals   2등\n"
     ]
    }
   ],
   "source": [
    "gpdf = df.groupby('Team')\n",
    "print(gpdf.get_group('Giants'))\n",
    "print(gpdf.get_group('Riders'))\n",
    "\n",
    "print(gpdf.agg({'Points':np.mean, 'Year':np.sum}))\n",
    "print(gpdf.agg({'Rank':lambda x:str(len(x))+'등'}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Team\n",
      "Devils    2\n",
      "Giants    1\n",
      "Kings     3\n",
      "Riders    4\n",
      "Royals    2\n",
      "Name: Rank, dtype: int64\n",
      "Team\n",
      "Devils    2\n",
      "Giants    4\n",
      "Kings     1\n",
      "Riders    1\n",
      "Royals    1\n",
      "Name: Rank, dtype: int64\n",
      "******************************\n",
      "        Year  Rank\n",
      "Team              \n",
      "Devils     2     2\n",
      "Giants     1     4\n",
      "Kings      3     1\n",
      "Riders     4     1\n",
      "Royals     2     1\n"
     ]
    }
   ],
   "source": [
    "# 팀별로 몇개씩 데이터가 있는가\n",
    "print(df.groupby('Team').Rank.count())\n",
    "# 팀별로 가장 좋은 랭킹은\n",
    "print(df.groupby('Team').Rank.min())\n",
    "\n",
    "gpdf = df.groupby('Team')\n",
    "print('*'*30)\n",
    "print(gpdf.agg({'Year':len, 'Rank':min}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>date</th>\n",
       "      <th>duration</th>\n",
       "      <th>item</th>\n",
       "      <th>month</th>\n",
       "      <th>network</th>\n",
       "      <th>network_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15/10/14 06:58</td>\n",
       "      <td>34.429</td>\n",
       "      <td>data</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>data</td>\n",
       "      <td>data</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15/10/14 06:58</td>\n",
       "      <td>13.000</td>\n",
       "      <td>call</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>Vodafone</td>\n",
       "      <td>mobile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>15/10/14 14:46</td>\n",
       "      <td>23.000</td>\n",
       "      <td>call</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>Meteor</td>\n",
       "      <td>mobile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>15/10/14 14:48</td>\n",
       "      <td>4.000</td>\n",
       "      <td>call</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>Tesco</td>\n",
       "      <td>mobile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>15/10/14 17:27</td>\n",
       "      <td>4.000</td>\n",
       "      <td>call</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>Tesco</td>\n",
       "      <td>mobile</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>825</td>\n",
       "      <td>825</td>\n",
       "      <td>13/03/15 00:38</td>\n",
       "      <td>1.000</td>\n",
       "      <td>sms</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>world</td>\n",
       "      <td>world</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>826</td>\n",
       "      <td>826</td>\n",
       "      <td>13/03/15 00:39</td>\n",
       "      <td>1.000</td>\n",
       "      <td>sms</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>Vodafone</td>\n",
       "      <td>mobile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>827</td>\n",
       "      <td>827</td>\n",
       "      <td>13/03/15 06:58</td>\n",
       "      <td>34.429</td>\n",
       "      <td>data</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>data</td>\n",
       "      <td>data</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>828</td>\n",
       "      <td>828</td>\n",
       "      <td>14/03/15 00:13</td>\n",
       "      <td>1.000</td>\n",
       "      <td>sms</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>world</td>\n",
       "      <td>world</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>829</td>\n",
       "      <td>829</td>\n",
       "      <td>14/03/15 00:16</td>\n",
       "      <td>1.000</td>\n",
       "      <td>sms</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>world</td>\n",
       "      <td>world</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>830 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index            date  duration  item    month   network network_type\n",
       "0        0  15/10/14 06:58    34.429  data  2014-11      data         data\n",
       "1        1  15/10/14 06:58    13.000  call  2014-11  Vodafone       mobile\n",
       "2        2  15/10/14 14:46    23.000  call  2014-11    Meteor       mobile\n",
       "3        3  15/10/14 14:48     4.000  call  2014-11     Tesco       mobile\n",
       "4        4  15/10/14 17:27     4.000  call  2014-11     Tesco       mobile\n",
       "..     ...             ...       ...   ...      ...       ...          ...\n",
       "825    825  13/03/15 00:38     1.000   sms  2015-03     world        world\n",
       "826    826  13/03/15 00:39     1.000   sms  2015-03  Vodafone       mobile\n",
       "827    827  13/03/15 06:58    34.429  data  2015-03      data         data\n",
       "828    828  14/03/15 00:13     1.000   sms  2015-03     world        world\n",
       "829    829  14/03/15 00:16     1.000   sms  2015-03     world        world\n",
       "\n",
       "[830 rows x 7 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Phone_Data.csv\n",
    "df = pd.read_csv('./D11/phone_data.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 830 entries, 0 to 829\n",
      "Data columns (total 7 columns):\n",
      "index           830 non-null int64\n",
      "date            830 non-null object\n",
      "duration        830 non-null float64\n",
      "item            830 non-null object\n",
      "month           830 non-null object\n",
      "network         830 non-null object\n",
      "network_type    830 non-null object\n",
      "dtypes: float64(1), int64(1), object(5)\n",
      "memory usage: 45.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>830.000000</td>\n",
       "      <td>830.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>414.500000</td>\n",
       "      <td>117.804036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>239.744656</td>\n",
       "      <td>444.129560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>207.250000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>414.500000</td>\n",
       "      <td>24.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>621.750000</td>\n",
       "      <td>55.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>829.000000</td>\n",
       "      <td>10528.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            index      duration\n",
       "count  830.000000    830.000000\n",
       "mean   414.500000    117.804036\n",
       "std    239.744656    444.129560\n",
       "min      0.000000      1.000000\n",
       "25%    207.250000      1.000000\n",
       "50%    414.500000     24.500000\n",
       "75%    621.750000     55.000000\n",
       "max    829.000000  10528.000000"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\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>index</th>\n",
       "      <th>date</th>\n",
       "      <th>duration</th>\n",
       "      <th>item</th>\n",
       "      <th>month</th>\n",
       "      <th>network</th>\n",
       "      <th>network_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15/10/14 06:58</td>\n",
       "      <td>34.429</td>\n",
       "      <td>data</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>data</td>\n",
       "      <td>data</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15/10/14 06:58</td>\n",
       "      <td>13.000</td>\n",
       "      <td>call</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>Vodafone</td>\n",
       "      <td>mobile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>15/10/14 14:46</td>\n",
       "      <td>23.000</td>\n",
       "      <td>call</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>Meteor</td>\n",
       "      <td>mobile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>15/10/14 14:48</td>\n",
       "      <td>4.000</td>\n",
       "      <td>call</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>Tesco</td>\n",
       "      <td>mobile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>15/10/14 17:27</td>\n",
       "      <td>4.000</td>\n",
       "      <td>call</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>Tesco</td>\n",
       "      <td>mobile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>15/10/14 18:55</td>\n",
       "      <td>4.000</td>\n",
       "      <td>call</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>Tesco</td>\n",
       "      <td>mobile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>16/10/14 06:58</td>\n",
       "      <td>34.429</td>\n",
       "      <td>data</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>data</td>\n",
       "      <td>data</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>16/10/14 15:01</td>\n",
       "      <td>602.000</td>\n",
       "      <td>call</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>Three</td>\n",
       "      <td>mobile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>16/10/14 15:12</td>\n",
       "      <td>1050.000</td>\n",
       "      <td>call</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>Three</td>\n",
       "      <td>mobile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>16/10/14 15:30</td>\n",
       "      <td>19.000</td>\n",
       "      <td>call</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>voicemail</td>\n",
       "      <td>voicemail</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index            date  duration  item    month    network network_type\n",
       "0      0  15/10/14 06:58    34.429  data  2014-11       data         data\n",
       "1      1  15/10/14 06:58    13.000  call  2014-11   Vodafone       mobile\n",
       "2      2  15/10/14 14:46    23.000  call  2014-11     Meteor       mobile\n",
       "3      3  15/10/14 14:48     4.000  call  2014-11      Tesco       mobile\n",
       "4      4  15/10/14 17:27     4.000  call  2014-11      Tesco       mobile\n",
       "5      5  15/10/14 18:55     4.000  call  2014-11      Tesco       mobile\n",
       "6      6  16/10/14 06:58    34.429  data  2014-11       data         data\n",
       "7      7  16/10/14 15:01   602.000  call  2014-11      Three       mobile\n",
       "8      8  16/10/14 15:12  1050.000  call  2014-11      Three       mobile\n",
       "9      9  16/10/14 15:30    19.000  call  2014-11  voicemail    voicemail"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10) #default는 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "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>index</th>\n",
       "      <th>date</th>\n",
       "      <th>duration</th>\n",
       "      <th>item</th>\n",
       "      <th>month</th>\n",
       "      <th>network</th>\n",
       "      <th>network_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>825</td>\n",
       "      <td>825</td>\n",
       "      <td>13/03/15 00:38</td>\n",
       "      <td>1.000</td>\n",
       "      <td>sms</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>world</td>\n",
       "      <td>world</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>826</td>\n",
       "      <td>826</td>\n",
       "      <td>13/03/15 00:39</td>\n",
       "      <td>1.000</td>\n",
       "      <td>sms</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>Vodafone</td>\n",
       "      <td>mobile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>827</td>\n",
       "      <td>827</td>\n",
       "      <td>13/03/15 06:58</td>\n",
       "      <td>34.429</td>\n",
       "      <td>data</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>data</td>\n",
       "      <td>data</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>828</td>\n",
       "      <td>828</td>\n",
       "      <td>14/03/15 00:13</td>\n",
       "      <td>1.000</td>\n",
       "      <td>sms</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>world</td>\n",
       "      <td>world</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>829</td>\n",
       "      <td>829</td>\n",
       "      <td>14/03/15 00:16</td>\n",
       "      <td>1.000</td>\n",
       "      <td>sms</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>world</td>\n",
       "      <td>world</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     index            date  duration  item    month   network network_type\n",
       "825    825  13/03/15 00:38     1.000   sms  2015-03     world        world\n",
       "826    826  13/03/15 00:39     1.000   sms  2015-03  Vodafone       mobile\n",
       "827    827  13/03/15 06:58    34.429  data  2015-03      data         data\n",
       "828    828  14/03/15 00:13     1.000   sms  2015-03     world        world\n",
       "829    829  14/03/15 00:16     1.000   sms  2015-03     world        world"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(10) #default는 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['data', 'call', 'sms'], dtype=object)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['item'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "item\n",
      "call    237.940722\n",
      "data     34.429000\n",
      "sms       1.000000\n",
      "Name: duration, dtype: float64\n",
      "item\n",
      "call    92321.00\n",
      "data     5164.35\n",
      "sms       292.00\n",
      "Name: duration, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 아이템별 사용시간의 평균\n",
    "print(df.groupby('item').duration.mean())\n",
    "# 합계\n",
    "print(df.groupby('item').duration.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<월별 [call] 누적시간\n",
      "> month\n",
      "2014-11    25547.0\n",
      "2014-12    13561.0\n",
      "2015-01    17070.0\n",
      "2015-02    14416.0\n",
      "2015-03    21727.0\n",
      "Name: duration, dtype: float64\n",
      "<월별 [data] 누적시간\n",
      "> month\n",
      "2014-11     998.441\n",
      "2014-12    1032.870\n",
      "2015-01    1067.299\n",
      "2015-02    1067.299\n",
      "2015-03     998.441\n",
      "Name: duration, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# call data 의 월별 사용시간 합계\n",
    "print('<월별 [call] 누적시간>\\n',df[df.item=='call'].groupby('month').duration.sum())\n",
    "print('<월별 [data] 누적시간>\\n',df[df.item=='data'].groupby('month').duration.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x8f2df88>]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RgS4X/dtw4BSpGVr0Tyl/lZF1kdcXJtO1SR1G2Ct2OVJXaZKoYMNjowkMEGbo2YRSfuul+Tu4mFvAq8MqfjlSV2mSqGDh1SvRp1UEMzemadE/pfzQsp0ZfLs1nYf6NKNpeKi7wymWJgk3GGm3cfzcJZbtzHB3KEqpCpR9KY/n5myjWUQof+jpnuVIXVVskhARm4gsE5FkEdkuIo9a7XVEZImIpFh/1rbaRUQmikiqiGwVkVin9xptbZ8iIqOd2juLSJK1z0Tx9POva9TLKvqn90wo5V/eXrKbw6cvuHU5UleVJMo84EljTGugKzBORNoAzwBLjTHNgaXW3wEGAM2txxhgEjiSCvACcD3QBXjhcmKxthnjtF//az80zxUUGMAdsdEs25VJxlkt+qeUP9h2+AyTV+3j7i4NiYtx33Kkrio2SRhj0o0xG63nWUAyEAUMAT63NvscGGo9HwJ8YRzWArVEJBLoBywxxpw0xpwClgD9rddqGGPWGEdhoy+c3stnxdujyS8wzNSif0r5vLz8AsbPSqJuaCWeGeDe5Uhd5dL5jojEAJ2AdUA9Y0w6OBIJEGFtFgU4X0dJs9qu1p5WSLtPaxIeSlxMbWZo0T+lfN5nP+8n6fAZXri9DTWruHc5UleVOEmISCgwE3jMGHP2apsW0mZK0V5YDGNEJFFEEjMzvX+lt3i7jb3Hz5OoRf+U8llpp7L5+/e76dMqgkHtI90djstKlCREJBhHgvjSGDPLaj5mXSrC+vPyVJ00wOa0ezRwpJj26ELa/4cx5iNjjN0YYw8P98w6J64YeLnon65ap5RPMsbw/NztiMBLQ9p6/D0RhSnJ7CYBJgPJxpi3nF6aB1yeoTQamOvUPsqa5dQVOGNdjloM9BWR2taAdV9gsfValoh0tT5rlNN7+bRqlYK4/boGLEjSon9K+aIFSen8uDODJ25tQXTtqu4Op1RKcibRDfg10EdENluPgcAbwK0ikgLcav0dYCGwF0gFPgYeBDDGnAReBhKsx0tWG8BY4BNrnz3AojI4Nq8wwm4j+1I+C7YWevKklPJSZ7Jz+cu8HbSPqsl9HrQcqauKLV5ujFlF4eMGADcXsr0BxhXxXlOAKYW0JwLtiovFF8U2rEWziFCmJRxiZFxDd4ejlCojb3y3k5Pnc/js/jiCAr3jnojCeG/kPsJR9C+ajQdPk5qR5e5wlFJlIGH/Sb5ef5AHujemXZRnLUfqKk0SHmB4bDRBAcL0xLTiN1ZKebScvHzGz0oiqlYVHr+1hbvDuWaaJDxAWGglbm4dwSwt+qeU1/vnir2kZpzjlaHtPHI5UldpkvAQ8VbRvx+16J9SXmtP5jne+zGV2zpE0rtVRPE7eAFNEh6iZ4twIqpX0nsmlPJSxhj+PCuJysEBPH+75y5H6ipNEh4iKDCAOzpHs2xXBse06J9SXmdGYhrr9p3kzwNbE1G9srvDKTOaJDxIvN1GgYGZG3UAWylvcvxcDq8uTKZLTB3i7bbid/AimiQ8SOOwanSJqcOMxDQt+qeUF3n52x1cuJTPa8PbERDgfaU3rkaThIeJj7Ox7/h5EvZr0T+lvMHyXRnM3XyEsb2a0iyiurvDKXOaJDzMwPb1Ca0UpKvWKeUFLi9H2iS8Gg/29o7lSF2lScLDVA0J4vbrIlmwNZ2si7nuDkcpdRXv/JBC2qkLvD6sPZWCAt0dTrnQJOGBRthtXMjN59ut6e4ORSlVhO1HzvDJqn3cFWfj+iZ13R1OudEk4YE62WrRPCJULzkp5aHyCwzjZyVRu2ow4we0dnc45UqThAdyFP2zsengaVKOadE/pTzNF2v2szXtDM/f3paaVb1rOVJXaZLwUMNio6yif3o2oZQnOXL6AhMW76JXy3Bu7+B9y5G6SpOEhwoLrcQtresxa+NhLuVp0T+lPIFjOdJtFBh4eUg7r1yO1FWaJDxYfFw0J85r0T+lPMV3247yQ7JjOVJbHe9cjtRVmiQ82E3Nw6lXo5JeclLKA5y9mMsL87bTtkEN7u8W4+5wKowmCQ8WFBjAHbHRLNeif0q53Zvf7eT4uRxeH97eq5cjdZX/HKmXulz075sNWvRPKXfZcOAk/157kPtubEyH6FruDqdCaZLwcDFh1ejSuA4zEg9p0T+l3OBSXsEvy5E+2df7lyN1lSYJLzDSbmP/iWzW7zvp7lCU8jsf/bSH3cfO8fLQtlSr5P3LkbpKk4QXGGAV/ZumA9hKVah9x88z8cdUBrWPpE+reu4Oxy00SXgBR9G/BixM0qJ/SlWUy8uRVgoK4AUfWo7UVcUmCRGZIiIZIrLNqa2jiKwVkc0ikigiXax2EZGJIpIqIltFJNZpn9EikmI9Rju1dxaRJGufieIPd6eUQrw9mou5BczfokX/lKoIMzceZs3eEzwzoBURNXxnOVJXleRM4jOg/xVtbwIvGmM6As9bfwcYADS3HmOASQAiUgd4Abge6AK8ICK1rX0mWdte3u/Kz1JAR1stWtTTon9KVYQT53J4ZcEO7I1qc3dcQ3eH41bFJgljzE/AlSOmBqhhPa8JHLGeDwG+MA5rgVoiEgn0A5YYY04aY04BS4D+1ms1jDFrjGPqzhfA0Gs+Kh90uejf5kOn2a1F/5QqV68sSOZ8Th6vD2/vc8uRuqq0YxKPAX8TkUPABGC81R4FOP+qm2a1Xa09rZB2VYhhnaIIDhSmJ+jZhFLlZWVKJrM3HWZsz6Y0r+d7y5G6qrRJYizwuDHGBjwOTLbaC0u5phTthRKRMdYYSGJmZqaLIXu/upeL/m3Son9KlYcLl/J5dvY2moRV48HezdwdjkcobZIYDcyyns/AMc4AjjMBm9N20TguRV2tPbqQ9kIZYz4yxtiNMfbw8PBShu7d4u02Tp6/xI87j7k7FKV8zsQfUzh4MptXh7WncrBvLkfqqtImiSNAT+t5HyDFej4PGGXNcuoKnDHGpAOLgb4iUtsasO4LLLZeyxKRrtasplHA3NIejD+4qUU49WtUZppeclKqTCWnn+Wjn/YSb4/mhqa+uxypq4q9fVBEvgZ6AWEikoZjltLvgHdEJAi4iGN2EsBCYCCQCmQD9wMYY06KyMtAgrXdS8aYy4PhY3HMoKoCLLIeqgiBAcIdnaOYtHwPR89cpH5N/52ap1RZubwcaa0qwfx5oG8vR+qqYpOEMebuIl7qXMi2BhhXxPtMAaYU0p4ItCsuDvUfIzrbeH/ZHmZuTGOcXjdV6pr9e+0BNh86zTt3daRW1RB3h+NR9I5rLxQTVo3rG9dheuIhCgq06J9S1yL9zAX+tngXPZqHMfi6Bu4Ox+NokvBSI+NsHDiRzfr9WvRPqWvxwtzt5BUU8OrQ9n6xHKmrNEl4qQHtIqleKUjvmVDqGny37Sjf7zjGY7e0oGFd/1iO1FWaJLxUlZBAbu/YgIXb0jmrRf+UclnWxVxemLeN1pE1eKB7Y3eH47E0SXixeLvNKvpX5K0lSqki/G3xLjKyHMuRBvvRcqSu0p7xYtdF16RlvepMT9SlTZVyxcaDp/jX2gOMviGGjjb/Wo7UVZokvJiIEB9nY8uh0+w6qkX/lCqJ3PwCxs9Mon6NyjzVr6W7w/F4miS83C9F/7SEuFIl8vHKvew6lsVLQ9oR6ofLkbpKk4SXq1MthFvb1GO2Fv1TqlgHTpznnR9SGNCuPre28c/lSF2lScIHjLCK/i1N1qJ/7mSM4Ys1+3n52x2cOJfj7nDUFYwxPDt7GyGBAfxlcFt3h+M19FzLB9zU3Cr6l3iIAe0j3R2OXzpzIZenv9nC4u2ORD098RCP3tycUTfEEBKkv4t5gtmbDrMq9TgvD21HPT9ejtRV+u31AYEBwp2do/lpdybpZy64Oxy/s+3wGW5/dxVLkzN4blBrljx+E50a1uaVBcn0/8dPLNuZ4e4Q/d7J85d4ZUEysQ1r8asu/r0cqas0SfiIEfZoCgzM3KDTYSuKMYYv1x1g+KSfyc0vYNrvu/LbHk1oXq86n98fx5T77ADc/1kCo6esJzVDZ6C5y6sLkjl7IZfXh3fw++VIXaVJwkc0qluNrk3qMD0xTYv+VYDzOXk8Nm0zz87eRtcmdVnwSA86N6rzy+siQp9W9fjusZt4blBrNh48Rb9/rOQv87ZzJlvvkK9IP6ceZ+bGNH7fswkt6+typK7SJOFDRsbZOHgym3X7tOhfeUo5lsWQ91czf8sRnry1BZ/dF0edaoWXlw4JCuC3PZqw/KlejIyz8cWa/fSasIx/rdlPXr7ORitvF3Pz+fPsJGLqVuXhPs3dHY5X0iThQ/q3tYr+6T0T5Wb2pjQGv7ea09mX+PcD1/Pwzc1LdPmibmglXhvWnm8f7kHL+tX5v7nbGTRxFatSjldA1P7r3R9T2H8im9d0OdJS0yThQ6qEBDK4YwMWJmnRv7J2MTef8bOSeHzaFtpH12ThIz24sVmYy+/TpkENvv5dVz68N5bs3DzunbyO332RyP7j58shav+262gW/1yxlztio0v1b6UcNEn4mHi7jZy8AuZt1qJ/ZWX/8fMM/+Bnvl5/kLG9mvLVb68n4hqmUIoI/dtFsuTxnvyxX0tWpx7n1rdX8PrCZLI0uZeJggLD+FlbqVElmGcH6XKk10KThI/pEF2TVvWrM0MvOZWJ77alc/u7qzh8+gJT7rPzp/6tCCqjiqGVgwMZ17sZy5/qxZCOUfzzp730nrCcaQkHydfJB9fky/UH2XjwNM8Nal3keJEqGU0SPkZEiLfb2JJ2hp1Hz7o7HK91Ka+Al+bv4A//3kiTiFAWPNKdPq3Kp4xDRI3KTBhxHXPHdaNR3Wr8aWYSg99bxXqdgFAqx85e5M1FO+neLIxhnaLcHY7X0yThg4ZeLvqXoPdMlMaR0xcY+dEapqzex303xjDj9zcQXbv8Vy27zlaLb/5wA+/c1ZGT5y8R/881jPtqI2mnssv9s33JX+Zt51J+Aa8Oa6fLkZYBTRI+qE61EPq2qc/sTWnk5OW7OxyvsmxXBoMmriTl2DnevyeWvwxuW6FlNUSEIR2j+PHJXjx2S3OWJh/j5r+v4O/f7yL7Ul6FxeGtluw4xqJtR3n0luY0qlvN3eH4BE0SPmqEPZpT2bn8sENLQpREXn4BExbv4v5PE6hXozLzHurGoA7uq4NVJSSQx25pwY9P9qJf2/q8+2MqvScsZ/YmvVmyKOdy8nh+7jZa1a/O73o0cXc4PkOThI/q0TycyJqV9Z6JEsjIusi9k9fx3rJURtptzBnXjSbhoe4OC4AGtaow8e5OzBx7A/VqVObxaVsYPulnNh085e7QPM6Exbs4evYir+lypGWq2J4UkSkikiEi265of1hEdonIdhF506l9vIikWq/1c2rvb7WlisgzTu2NRWSdiKSIyDQR0akIZeCXon8pmRw5rUX/irJmzwkGTVzF5kOnmTDiOv56ZwePvOmqc6M6zHmwGxNGXMfh0xcY9sHPPD5tM0fPXHR3aB5h86HTfL5mP6O6NiK2YW13h+NTSpJuPwP6OzeISG9gCNDBGNMWmGC1twHuAtpa+3wgIoEiEgi8DwwA2gB3W9sC/BV42xjTHDgFPHCtB6UcRnS2YbToX6EKCgzvL0vlV5+spXrlIOaM68adnaPdHdZVBViJf9lTvXiwV1MWJKXTe8Jy3l2awsVc/x17ys0vYPysJOpV1+VIy0OxScIY8xNw5Vy8scAbxpgca5vLF76HAFONMTnGmH1AKtDFeqQaY/YaYy4BU4Eh4ph60Af4xtr/c2DoNR6TsjSsW5UbmtRl+oZDeh3byanzl3jg8wT+tngXgzo0YN5D3WlVv4a7wyqx0EpBPN2/FUuf6EmvluH8fclubv77ChZsTccY//t3nrxqH8npZ3lxSFuqVw52dzg+p7QX7loAPazLRCtEJM5qjwKcL4KnWW1FtdcFThtj8q5oV2VkZJyNQycvsHbfCXeH4hE2HTzFbe+uYnXqCV4e0paJd3X02nWObXWqMuneznz9u65UrxzEuK82MvKfa9l2+Iy7Q6swB09k848fdtO3TT36ta3v7nB8UmmTRBBQG+gK/BGYbp0VFDYp2ZSivVAiMkZEEkUkMTMz0/Wo/VD/dvWpXjmI6Qn+PYBtjOHT1Z5/Eg8AABFoSURBVPuI/+caROCbsTfw6xtifGIe/Q1NHaXKXxvWntTMc9z+3ir+9M1WMrN8ewlVYwzPzkkiKCCAF4focqTlpbRJIg2YZRzWAwVAmNVuc9ouGjhylfbjQC0RCbqivVDGmI+MMXZjjD08PLyUofuXysGBDOnYgEXbjnLmgn/WBTp7MZdxX23kxfk76NkiggUP96BDdC13h1WmAgOEe65vyLKnevFAt8bM3JhG7wnL+eeKPT57r8y8LUdYmXKcP/ZrSWTNKu4Ox2eVNknMwTGWgIi0AEJw/MCfB9wlIpVEpDHQHFgPJADNrZlMITgGt+cZxwXUZcCd1vuOBuaW9mBU4X4p+rfF/4r+7ThylsHvrmLx9mOMH9CKj0d1pmZV371uXbNKMM/d1obvH7+J6xvX4fVFO+n79k98v/2oT41XnM6+xEvzd9DRVot7uzZydzg+rSRTYL8G1gAtRSRNRB4ApgBNrGmxU4HR1lnFdmA6sAP4DhhnjMm3xhweAhYDycB0a1uAPwFPiEgqjjGKyWV7iKp9lKPonz9dcjLGMC3hIMM+WM2F3HymjunK73s29YnLSyXRJDyUyffF8flvuhAcGMCYf23g15PXs+uobyyh+trCZM5cyOX14e0J1OVIy5V4628XdrvdJCYmujsMr/Hp6n28OH8HCx/pQZsG3jOTpzSyL+Xx3JxtzNp4mO7NwvjHXR0JC63k7rDcJje/gC/XHuDtH1LIupjLr65vxBO3tqC2l1ZHXbPnBHd/vJaxvZryp/6t3B2O1xGRDcYYe0m319sS/cTQjlGEBAb4/B3YqRnnGPr+amZvOsxjtzTn89908esEARAcGMB93Rqz/Kle3Nu1EV+tP0ivCcv5dPU+cr1sCdWLufk8OzuJRnWr8ujNuhxpRdAk4SdqVwvh1rb1mLP5sM8OZM7dfJjB763i+LlLfPGbLjx2Swu9FOGkdrUQXhrSjkWP9qBDdE1enL+DAe+sZPku76nv9cGyVPYeP8+rQ3U50oqiScKPxNttnM7OZcmOY+4OpUzl5OXz3JwkHp26mTaRNVj4SA96NNfZb0VpUa86X/ymCx+PspOXX8B9nybwm88S2JN5zt2hXVXKsSwmrdjD8E5RdG+uy5FWFE0SfqR7szAa1KzM9ETfKdNx8EQ2d05aw7/XHuT3NzXh6zFdqV+z9EuL+gsR4dY29Vj8+E38eWArEvadpN/bP/Hytzs8cqq0YznSJEIrBelypBVMk4QfuVz0b2VKJod9oOjf99uPMujdlRw4cZ6PR9kZP7C1Vv90UaWgQMbc1JRlf+zFCHs0U1bvo/eE5Xy57oBHLaH6dcJBEg+c4tlBbajr52NMFU3/R/mZEXbvL/qXm1/AawuTGfOvDcTUrcaCR3pwa5vyWVrUX4SFVuL14R2Y/1B3mkWE8uzsbQyauJKf9xx3d2hknL3IG4t2cmPTutwRq1V7KpomCT9jq1OVG5vWZXqidxb9Sz9zgbs/WstHP+3l110b8c3YG7DVKf+lRf1Fu6iaTBvTlQ9+Fcu5nDzu+Xgdf/jXBg6ecN8Sqi/O30FOXgGvDmvvN/e5eBJNEn5oZJyNtFMXWLvXu4r+rUzJZNDEVexIP8s7d3Xk5aHtqBSkM1zKmogwsH0kPzzRkz/2a8lPKZnc8tYK/vrdTs7lVOwSqkuTj7EgKZ1Hb25O4zBdjtQdNEn4oX5tHUX/pnnJPRP5BYa3l+xm1JT1hIdWYt5D3RnSUS87lLfKwYGM692MZU/14rbrIpm0fA+9JyxnRgWdhZ7PyeP5udtpUS9UlyN1I00SfqhycCBDO0Y5iv5le95MFmfHz+Uwesp63lmawvBO0cwZ141mEZ6xtKi/qFejMm/Fd2TOuG5E167CH7/ZytAPVpO4/8plZsrWW0t2c+TMBV4f3oGQIP1R5S7a835qZJyNS3kFzNty2N2hFGn9vpMMmriShP0nefOODkwY0YEqIXp5yV062moxa+yN/GNkRzLO5nDnh2t4+OtN5TJTLintDJ+u3sevrm9I50a6HKk7aZLwU20b1KB1ZA2PvORUUGD4cMUe7v54LVWCA5n9YDfi42w6aOkBRIShnaL48amePHJzc77ffpSb/76ct5bsJvtS2YxX5OUX8MysrYSFVuJprc3kdpok/JSIMNIezbbDZ9l+xHNWMjuTncuYfyXyxqKd9Gtbj/kPd/f5goTeqGpIEE/c2oKlT/bkltb1mLg0hZv/voK5mw9fc0nyT1fvZ/uRs7w4uC01dDlSt9Mk4ceGWEX/ZnjIHdhbDp1m0LsrWbE7k7/c3ob374nVNYs9XHTtqrx3Tywz/nADdUNDeHTqZu6Y9DNbDp0u1fsdOpnNW0t2c0vrevRvp8uRegJNEn6sdrUQ+ratx+xNh7mY676if8YYvliznxEfrsEYmP77G7ivW2O9vORF4mLqMG9cd968owMHT15gyPureXL6Fo6dvVji9zDG8NycbQQIvDSkrf77ewhNEn4u3m7jzAX3Ff07l5PHI1M38/zc7XRrVpdvH+5Op4Y6UOmNAgKE+Dgby57qyR96NmX+liP0nrCc95elluiXkPlb01mxO5On+rWkQS1djtRTaJLwc92ahRFVq4pb1pnYedSxtOiCrUd4un9LJo+O89qFcNR/VK8czDMDWrHkiZvo3iyMvy3exS1vrWBRUnqR4xVnsnN5af52rouuyagbYio2YHVVmiT8XGCAcEfnaFalHiftVMWVXpiReIih768mKyePr37XlQd7NSNA137wKY3qVuOjUXa++u31hFYKYuyXG7nro7WFTpR4fVEyp7JzeX14B10DxMNoklCM6BxtFf0r/3smLlzK5+lvtvDHb7YS27A2Cx/pQdcmdcv9c5X73NgsjG8f7s7LQ9ux+1gWt727ivGzkjh+LgeAdXtPMDXhEL/t0VhnsnkgXeNaAfCrT9Zy4EQ2P/2xd7n9Rr838xwPfrmRnUezeLhPM105zg+dyc7lnaUpfLFmP1WCA3n45mZMSzjEpfwCvn+sp94sWQF0jWtVKvF2R9G/n/eUT9G/BVvTGfzeao6dvchn98fxZN+WmiD8UM2qwTx/exu+e+wm7DG1eW3hTvZknueVoe01QXioIHcHoDxDv7b1qVE5iOmJh8p0achLeY61Hz77eT+xDWvx3j2xOnNF0SwilE/v78KK3ZkcO3uRni10uVlPpUlCAVbRv05RTE04xJnsXGpWvfab2NJOZTPuq01sOXSaB7o35k/9W2mhNvVfNDl4Pv0fq34Rb3cU/ZtbBkX/liYfY9DEVezNOMeH93bm/25rowlCKS9U7P9aEZkiIhkisq2Q154SESMiYdbfRUQmikiqiGwVkVinbUeLSIr1GO3U3llEkqx9JoreZuk27aJq0iayBtMSSn/PRF5+AW8s2skDnycSXbsK3z7SXcsrKOXFSvKr3WdA/ysbRcQG3AocdGoeADS3HmOASda2dYAXgOuBLsALInL5ttpJ1raX9/ufz1IVZ2Scje1HzrLtsOtF/46dvcg9n6zjwxV7uOf6hswceyON6upqYkp5s2KThDHmJ6Cw1UXeBp4GnOfQDgG+MA5rgVoiEgn0A5YYY04aY04BS4D+1ms1jDFrjGMu7hfA0Gs7JHUthnRsQEhQADNcvAP759TjDJq4kqS0M7w98jpeG9aeysE6W0Upb1eqi8QiMhg4bIzZcsVLUYDzT5c0q+1q7WmFtCs3qVU1hH5t6zNn85ES1dspKDBMXJrCvZPXUatqCPMe6sawTtEVEKlSqiK4nCREpCrwLPB8YS8X0mZK0V7UZ48RkUQRSczMzCxJuKoU4u3RnLmQy/fFFP07cS6H+z5L4K0luxl8XQPmjutG83rVKyhKpVRFKM2ZRFOgMbBFRPYD0cBGEamP40zA5rRtNHCkmPboQtoLZYz5yBhjN8bYw8N16lx56dbUUfTvapecNhw4yaCJq1i79wSvDWvP2yM7Uq2SzqhWyte4nCSMMUnGmAhjTIwxJgbHD/pYY8xRYB4wyprl1BU4Y4xJBxYDfUWktjVg3RdYbL2WJSJdrVlNo4C5ZXRsqpQCAoQ7iyj6Z4zhk5V7GfnPtYQEBTBr7I3cc31Drf2vlI8qyRTYr4E1QEsRSRORB66y+UJgL5AKfAw8CGCMOQm8DCRYj5esNoCxwCfWPnuARaU7FFWWRtgdJ3jOq9aduZDLH/69gVcWJHNz6wi+faQ77aJquitEpVQF0AJ/qkj3frKOfcfPs/Lp3uxIP8uDX27kyOkLPDOgFQ9015XjlPJGWuBPlZn4OBuHT1/g2TlJDJ/0M7n5BUz7/Q38tkcTTRBK+QkdaVRF6tumHjWrBPP1+kPc1CKcf4zsSB1dOU4pv6JJQhWpcnAgrw1rT2bWRUbdEKMrxynlhzRJqKsa1CHS3SEopdxIxySUUkoVSZOEUkqpImmSUEopVSRNEkoppYqkSUIppVSRNEkopZQqkiYJpZRSRdIkoZRSqkheW+BPRDKBA6XcPQw4Xobh+DrtL9dof7lG+8s119pfjYwxJV6Qx2uTxLUQkURXqiD6O+0v12h/uUb7yzUV3V96uUkppVSRNEkopZQqkr8miY/cHYCX0f5yjfaXa7S/XFOh/eWXYxJKKaVKxl/PJJRSSpWAVyQJEbGJyDIRSRaR7SLyqNVeR0SWiEiK9Wdtq72ViKwRkRwReaqQ9wsUkU0i8u1VPvM7ETl95TYi8pCIpIqIEZGwsj7WsuBh/fWliOwSkW0iMkVEgsv6eK9VWfaXiOwXkSQR2SwiRS7CLiL9rX5JFZFnnNr96vtVBv2l36/CP7Oo/posIltEZKuIfCMiocUegDHG4x9AJBBrPa8O7AbaAG8Cz1jtzwB/tZ5HAHHAq8BThbzfE8BXwLdX+cybgduv3AboBMQA+4Ewd/eNF/TXQECsx9fAWHf3T3n2V0m+F0AgsAdoAoQAW4A2/vj9KoP+0u+Xa/1Vw2m7ty5//tUeXnEmYYxJN8ZstJ5nAclAFDAE+Nza7HNgqLVNhjEmAci98r1EJBoYBHxSzGcuBbIKad9kjNlf6oOpAB7WXwuNBVgPRJf2uMpLWfZXCXUBUo0xe40xl4Cp1mf53ferhK7WX/r9+l9X66+zACIiQBWg2EFpr0gSzkQkBsdvW+uAesaYdHD8Q+DIwMX5B/A0UFBOIXoUT+kv6zLAr4HvruV9ylsZ9JcBvheRDSIypohtooBDTn9Ps9q8jqf0l36//stV+0tEPgWOAq2Ad4v7QK9KEtb1s5nAY5czoov73wZkGGM2lHlwHsjD+usD4CdjzMoyeK9yca39ZelmjIkFBgDjROSmwj6qkDavm2boYf2l3y+njyqk7Zf+MsbcDzTAcUYzsrgP9JokYf2mMBP40hgzy2o+JiKR1uuRQEYxb9MNGCwi+3GcgvURkX+LyPXWQNBmERlcTodQoTypv0TkBSAcx9iGRyqj/sIYc8T6MwOYDXSxBi4v99cfcPxmZ3PaLRo4UnZHU/48qb/0++X698sYkw9MA+4o7jO9IklY188mA8nGmLecXpoHjLaejwbmXu19jDHjjTHRxpgY4C7gR2PMvcaYdcaYjtZjXjkcQoXypP4Skd8C/YC7jTEeeYmvrPpLRKqJSPXLz4G+wDZjzCGn/voQSACai0hjEQnB0bde873zpP7S71fJ+0scmjnFdDuws9gDKGpE25MeQHccp0tbgc3WYyBQF1gKpFh/1rG2r48jm54FTlvPa1zxnr24+mydlUAmcMHav5/V/oj19zwc2fkTd/ePh/dXHo6ZFpfjeN7d/VNe/YVjNskW67EdePYqnzkQxyyXPc7b+dP3q4z6S79fJewvHCcFq4EkYBvw5ZX/zwt76B3XSimliuQVl5uUUkq5hyYJpZRSRdIkoZRSqkiaJJRSShVJk4RSSqkiaZJQSilVJE0SSimliqRJQimlVJH+H7ef+/YJ0PQVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cperm = df[df.item=='call'].groupby('month').duration.sum()\n",
    "dperm = df[df.item=='data'].groupby('month').duration.sum()\n",
    "plt.plot(cperm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9242408>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "cperm.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9176e48>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "dperm.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x9216d08>]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(dperm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "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>index</th>\n",
       "      <th>date</th>\n",
       "      <th>duration</th>\n",
       "      <th>item</th>\n",
       "      <th>month</th>\n",
       "      <th>network</th>\n",
       "      <th>network_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15/10/14 06:58</td>\n",
       "      <td>34.429</td>\n",
       "      <td>data</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>data</td>\n",
       "      <td>data</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15/10/14 06:58</td>\n",
       "      <td>13.000</td>\n",
       "      <td>call</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>Vodafone</td>\n",
       "      <td>mobile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>15/10/14 14:46</td>\n",
       "      <td>23.000</td>\n",
       "      <td>call</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>Meteor</td>\n",
       "      <td>mobile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>15/10/14 14:48</td>\n",
       "      <td>4.000</td>\n",
       "      <td>call</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>Tesco</td>\n",
       "      <td>mobile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>15/10/14 17:27</td>\n",
       "      <td>4.000</td>\n",
       "      <td>call</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>Tesco</td>\n",
       "      <td>mobile</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>825</td>\n",
       "      <td>825</td>\n",
       "      <td>13/03/15 00:38</td>\n",
       "      <td>1.000</td>\n",
       "      <td>sms</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>world</td>\n",
       "      <td>world</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>826</td>\n",
       "      <td>826</td>\n",
       "      <td>13/03/15 00:39</td>\n",
       "      <td>1.000</td>\n",
       "      <td>sms</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>Vodafone</td>\n",
       "      <td>mobile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>827</td>\n",
       "      <td>827</td>\n",
       "      <td>13/03/15 06:58</td>\n",
       "      <td>34.429</td>\n",
       "      <td>data</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>data</td>\n",
       "      <td>data</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>828</td>\n",
       "      <td>828</td>\n",
       "      <td>14/03/15 00:13</td>\n",
       "      <td>1.000</td>\n",
       "      <td>sms</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>world</td>\n",
       "      <td>world</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>829</td>\n",
       "      <td>829</td>\n",
       "      <td>14/03/15 00:16</td>\n",
       "      <td>1.000</td>\n",
       "      <td>sms</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>world</td>\n",
       "      <td>world</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>830 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index            date  duration  item    month   network network_type\n",
       "0        0  15/10/14 06:58    34.429  data  2014-11      data         data\n",
       "1        1  15/10/14 06:58    13.000  call  2014-11  Vodafone       mobile\n",
       "2        2  15/10/14 14:46    23.000  call  2014-11    Meteor       mobile\n",
       "3        3  15/10/14 14:48     4.000  call  2014-11     Tesco       mobile\n",
       "4        4  15/10/14 17:27     4.000  call  2014-11     Tesco       mobile\n",
       "..     ...             ...       ...   ...      ...       ...          ...\n",
       "825    825  13/03/15 00:38     1.000   sms  2015-03     world        world\n",
       "826    826  13/03/15 00:39     1.000   sms  2015-03  Vodafone       mobile\n",
       "827    827  13/03/15 06:58    34.429  data  2015-03      data         data\n",
       "828    828  14/03/15 00:13     1.000   sms  2015-03     world        world\n",
       "829    829  14/03/15 00:16     1.000   sms  2015-03     world        world\n",
       "\n",
       "[830 rows x 7 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "network\n",
      "Meteor        87\n",
      "Tesco         84\n",
      "Three        215\n",
      "Vodafone     215\n",
      "data         150\n",
      "landline      42\n",
      "special        3\n",
      "voicemail     27\n",
      "world          7\n",
      "Name: item, dtype: int64\n",
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     ]
    },
    {
     "data": {
      "text/plain": [
       "0      data\n",
       "1      call\n",
       "6      data\n",
       "13     data\n",
       "26     data\n",
       "       ... \n",
       "821    data\n",
       "822    data\n",
       "823    data\n",
       "824    data\n",
       "827    data\n",
       "Name: item, Length: 151, dtype: object"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#통신사별 이용건수\n",
    "print(df.groupby('network').item.count())\n",
    "# 시간대별 이용건수\n",
    "#df.date[0].split()[1][:2]\n",
    "\n",
    "Temp = []\n",
    "for d in df.date:\n",
    "    Temp.append(d.split()[1][:2])\n",
    "print(Temp)\n",
    "df['hour'] = Temp\n",
    "df.head()\n",
    "iperh = df.groupby('hour').item.count()\n",
    "# 시간대별 평균 이용시간\n",
    "dperh = df.groupby('hour').duration.mean()\n",
    "df[df.hour=='06'].item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9364cc8>"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TSe4ErklyT5KXzFN7XZK3JHnGfPuZo346yRVJPprkkCSXJflekq8mecGA+j2SvDXJLX3dTJKrk5wysAsXAg8CR1bVPlW1D/Av+7ZPDtj/4XOcfpYuk+bzIbpv538KOCHJp5Ls3q970YD684BbgXvpHn//SJd//5th2fFIVbWiTsDXt7HuGwPqHwbuBO4aOW1d/sGA+htHLq+i+5zpp4HdgesX0n/gq3Nd9zbqr+/P/xlwEvB5YIbuH+eXh9b3lzfPtW6+v71fvro/3x3YNPD++yLdE9kBI20H9G2XDag/fI7TzwL3D6j/FHA23RPkJf3y7v266wbUXwqcDpwJ3Nj3+6l928UD6n8063/vLuCHW/8PB9RfN3L5g8DbgKcBvw18ZkD9TSOXrwD+eX/5J4GN89TeBbwL2Ax8pd/nU4bc7339V+gGEifSBdTxffvRwJcH1F8MnEL3jfbfAf4zsAY4H/iDAfXbyo45141s8zDwV/3tNvv0jwPqb5i1/Gbg/wL7DPzf29Zj94b56h91fQstWO4T3WjzlcBjRtoeA/xb4JoB9bcDT51j3b0D6m8b03ZWfyfdPqD+7XTPwE8Hfq9/gDwVOBX47ID6R/0TAHsDrwb+akD9tf0D+Qi6Aw5N9+2HMc+TS1/7jP7y4cBVI+tuHXj/7dIPMOANdE8QPzPSdteQ2272/T/mbxmy/9uAVf3lq2etu2kB+/4F4P3At/rbfv2Et92QgdHXZi1/tT9/zLjH5Zj6LwFvBPYfaduf7gn6LwfU3wysmWPdkOzYxEhu9W3rgFuAexby9wNvW8h9N/b6Flqw3CdgNfAJ4AHgG/3pgb7t0AH1rwWeN8e60wfUf5TuiJaz238D+OHAv+EU4Bq6Efff0b3U+gPgyQNqrxqyj23UH013hM1NwM/TjVxv72/DY+epPYpu1PYNulHcC/v2KeCdA/e/yz/A6EaenwTeTfcKbN4R+0jtFrpR6+/SveLMyLohr/xO7++Do+imNv8I+EW6qc2PzFM7bmCxG7AW+NCAfX8Z+GW6wdk9wHF9+0uY51VDv91fAz/fX/5V4Isj64YMDPYC3kH3BPcg8J3+/+EdwN4D6o8HnjnHuuMG1L8T+KUx7WsZNjB8K7DHmPbDgIuG/g9tPa3Ib6gm+Sm6478fRPfPtZnuJfGmgfXPGqkvuoOWXbKd64+bVb89+7/19ntK3/RAv/+bBtb+G7qplIcXse+96KY0jgW2vpH4bbopkrOr6sF56o+nC9FHHQI6yXFV9Zl56t8JfKmq/nJW+1rgf1TVmnnq30r3RPb9We2H9f0/flv1s2p+le6Vw+qqOmBgzVmzmt5fVTNJDuj7dfKA6zgSeA3dK7hVdFMkn6E7DMhD26i7oKpOGNLPOeqfRxdwP6J7xfoauifWbwL/oar+ep7659JNRf0k3ZP8q6rqG0mmgBOr6r0D+vAsuifXq0fvwyRrq+rSgfUH0c0SLGX9MVU15D2jifb/CAt9NljuE90I7/r+/Nf705nADcCZA+rfuHXbHVR/Rr/tjuz/om6/SWoH3ren7mr1wBOA5+ys/V8J+x5aD/wm3avWzwB3M/JKlWFTcpPWn74j6x91fZPc4MtxopsSeOyY9scx7KWN9Yusn3TfA/q22fqds35n6DtwE/20Bt307kbg9f3ykDn/nbp+9mm5fqxjEj+im064Z1b7gf0665evftJ9k+TGuVbRzb1bv0Lrd+a+93arfiqjqu7up6cuSvI0xv+AUGv1j7ASw/23gMuT3E43Vwjdp00OA143Z5X1S1E/6b6hexD+Ct0bWqNC94aZ9Su3fmfuO8C3kjy/qm4AqKrvJ3k53fc75v0CYQP1j7Diwr2qLk2y9aN8B9HdsVvoPhb1sPXLVz/pvnufpXtpecPsFUmutH5F1+/MfQc4GXjEG8bVvYF8cpI/3QXqH2FFflpGkjSZlXj4AUnShAx3SWqQ4a5dUpLVSW7e0f2QlovhLi2RJCvuAwradRnu2pXtlu5497ck+VKSJyR5fn+Y2RuT/EV/OAWSXJlkur+8b5K7+8unpDtW/v+kO6aLtCIY7tqVrQHeV1U/DXyX7pg6HwbOqKrn0n1jcPaxXsb5OWBdVR21bD2VFshw167srpHPVF8LPAPYs6r+V992Pt0RFedzWVV9Zzk6KC2W4a5d2T+NXH4Y2NYvRT3Ejx8vj5+17u+XslPSUjDcpR/7HvBgkl/ol08Cto7i76b7NSjojvstrWi+uy890jrgT5I8ke7HMk7t298FXJjkJLpfipJWNA8/IEkNclpGkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1KD/D6/9HwXZgsvnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "iperh.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa4c0d48>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "dperh.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dperh.plot(kind='pie')\n",
    "\n",
    "plt.savefig('frequencytime.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "month\n",
      "2014-11    26639.441\n",
      "2014-12    14641.870\n",
      "2015-01    18223.299\n",
      "2015-02    15522.299\n",
      "2015-03    22750.441\n",
      "Name: duration, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# month별로 사용시간의 sum을 구하면\n",
    "print(df.groupby('month').duration.sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### multi group by"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "month    item\n",
      "2014-11  call    25547.000\n",
      "         data      998.441\n",
      "         sms        94.000\n",
      "2014-12  call    13561.000\n",
      "         data     1032.870\n",
      "         sms        48.000\n",
      "2015-01  call    17070.000\n",
      "         data     1067.299\n",
      "         sms        86.000\n",
      "2015-02  call    14416.000\n",
      "         data     1067.299\n",
      "         sms        39.000\n",
      "2015-03  call    21727.000\n",
      "         data      998.441\n",
      "         sms        25.000\n",
      "Name: duration, dtype: float64\n",
      "hour  item\n",
      "00    call      4\n",
      "      sms       6\n",
      "01    call      1\n",
      "      sms       6\n",
      "02    call      4\n",
      "      sms       1\n",
      "06    call      1\n",
      "      data    150\n",
      "07    call      2\n",
      "      sms       5\n",
      "08    call      5\n",
      "      sms      13\n",
      "09    call     22\n",
      "      sms      16\n",
      "10    call     23\n",
      "      sms      14\n",
      "11    call     20\n",
      "      sms      23\n",
      "12    call     24\n",
      "      sms      25\n",
      "13    call     32\n",
      "      sms      20\n",
      "14    call     43\n",
      "      sms      13\n",
      "15    call     29\n",
      "      sms       8\n",
      "16    call     29\n",
      "      sms      16\n",
      "17    call     40\n",
      "      sms      17\n",
      "18    call     33\n",
      "      sms      28\n",
      "19    call     31\n",
      "      sms      16\n",
      "20    call     26\n",
      "      sms       8\n",
      "21    call     13\n",
      "      sms      17\n",
      "22    call      4\n",
      "      sms      23\n",
      "23    call      2\n",
      "      sms      17\n",
      "Name: date, dtype: int64\n",
      "item  month  \n",
      "call  2014-11    25547.000\n",
      "      2014-12    13561.000\n",
      "      2015-01    17070.000\n",
      "      2015-02    14416.000\n",
      "      2015-03    21727.000\n",
      "data  2014-11      998.441\n",
      "      2014-12     1032.870\n",
      "      2015-01     1067.299\n",
      "      2015-02     1067.299\n",
      "      2015-03      998.441\n",
      "sms   2014-11       94.000\n",
      "      2014-12       48.000\n",
      "      2015-01       86.000\n",
      "      2015-02       39.000\n",
      "      2015-03       25.000\n",
      "Name: duration, dtype: float64\n",
      "month    2014-11   2014-12    2015-01    2015-02    2015-03\n",
      "item                                                       \n",
      "call   25547.000  13561.00  17070.000  14416.000  21727.000\n",
      "data     998.441   1032.87   1067.299   1067.299    998.441\n",
      "sms       94.000     48.00     86.000     39.000     25.000\n",
      "<class 'pandas.core.series.Series'>\n",
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "# 월별 아이템별 사용시간 multi gruopby Hir\n",
    "print(df.groupby(['month', 'item']).duration.sum())\n",
    "print(df.groupby(['hour', 'item']).date.count())\n",
    "# 아이텝별 월별 사용시간\n",
    "print(df.groupby(['item', 'month']).duration.sum())\n",
    "print(df.groupby(['item', 'month']).duration.sum().unstack())\n",
    "print(type(df.groupby(['hour', 'month']).duration.sum()))\n",
    "print(type(df.groupby(['hour', 'month']).duration.sum().unstack()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### apply()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
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       "  <tbody>\n",
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       "      <td>10</td>\n",
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       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    a   b   c\n",
       "0  10  20  50\n",
       "1  20  30  60\n",
       "2  30  40  70"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'a':[10,20,30],\n",
    "                  'b':[20,30,40],\n",
    "                  'c':[50,60,70]\n",
    "                 })\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1\n",
      "1    2\n",
      "2    3\n",
      "Name: a, dtype: int64\n",
      "0    10\n",
      "1    12\n",
      "2    14\n",
      "Name: c, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# df['a']컬럼을 10으로 나눈 정수값으로 바꾸고 싶다면\n",
    "\n",
    "#df['a'] = (df['a']//10)\n",
    "#df['a'] //= 10\n",
    "\n",
    "\n",
    "def div10(x):\n",
    "    return int(x/10)\n",
    "\n",
    "print(df['a'].apply(div10))\n",
    "# df의 'c'column을 5로 나눈 정수로 바꿔주기\n",
    "def div5(x):\n",
    "    return int(x/5)\n",
    "print(df['c'].apply(div5))\n",
    "df['c'] = df['c'].apply(div5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    2\n",
      "1    2\n",
      "2    2\n",
      "Name: c, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# apply()와 lmabda함수 결합\n",
    "# df['c']에 \n",
    "print(df['c'].apply(lambda x: int(x/5)))\n",
    "df['c'] = df['c'].apply(lambda x: int(x/5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th>index</th>\n",
       "      <th>date</th>\n",
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       "      <th>item</th>\n",
       "      <th>month</th>\n",
       "      <th>network</th>\n",
       "      <th>network_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15/10/14 06:58</td>\n",
       "      <td>34.429</td>\n",
       "      <td>data</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>data</td>\n",
       "      <td>data</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15/10/14 06:58</td>\n",
       "      <td>13.000</td>\n",
       "      <td>call</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>Vodafone</td>\n",
       "      <td>mobile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>15/10/14 14:46</td>\n",
       "      <td>23.000</td>\n",
       "      <td>call</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>Meteor</td>\n",
       "      <td>mobile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>15/10/14 14:48</td>\n",
       "      <td>4.000</td>\n",
       "      <td>call</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>Tesco</td>\n",
       "      <td>mobile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>15/10/14 17:27</td>\n",
       "      <td>4.000</td>\n",
       "      <td>call</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>Tesco</td>\n",
       "      <td>mobile</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>825</td>\n",
       "      <td>825</td>\n",
       "      <td>13/03/15 00:38</td>\n",
       "      <td>1.000</td>\n",
       "      <td>sms</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>world</td>\n",
       "      <td>world</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>826</td>\n",
       "      <td>826</td>\n",
       "      <td>13/03/15 00:39</td>\n",
       "      <td>1.000</td>\n",
       "      <td>sms</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>Vodafone</td>\n",
       "      <td>mobile</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>827</td>\n",
       "      <td>827</td>\n",
       "      <td>13/03/15 06:58</td>\n",
       "      <td>34.429</td>\n",
       "      <td>data</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>data</td>\n",
       "      <td>data</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>828</td>\n",
       "      <td>828</td>\n",
       "      <td>14/03/15 00:13</td>\n",
       "      <td>1.000</td>\n",
       "      <td>sms</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>world</td>\n",
       "      <td>world</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>829</td>\n",
       "      <td>829</td>\n",
       "      <td>14/03/15 00:16</td>\n",
       "      <td>1.000</td>\n",
       "      <td>sms</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>world</td>\n",
       "      <td>world</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>830 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index            date  duration  item    month   network network_type\n",
       "0        0  15/10/14 06:58    34.429  data  2014-11      data         data\n",
       "1        1  15/10/14 06:58    13.000  call  2014-11  Vodafone       mobile\n",
       "2        2  15/10/14 14:46    23.000  call  2014-11    Meteor       mobile\n",
       "3        3  15/10/14 14:48     4.000  call  2014-11     Tesco       mobile\n",
       "4        4  15/10/14 17:27     4.000  call  2014-11     Tesco       mobile\n",
       "..     ...             ...       ...   ...      ...       ...          ...\n",
       "825    825  13/03/15 00:38     1.000   sms  2015-03     world        world\n",
       "826    826  13/03/15 00:39     1.000   sms  2015-03  Vodafone       mobile\n",
       "827    827  13/03/15 06:58    34.429  data  2015-03      data         data\n",
       "828    828  14/03/15 00:13     1.000   sms  2015-03     world        world\n",
       "829    829  14/03/15 00:16     1.000   sms  2015-03     world        world\n",
       "\n",
       "[830 rows x 7 columns]"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phone_df  = pd.read_csv('./D11/phone_data.csv')\n",
    "phone_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>date</th>\n",
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       "      <th>item</th>\n",
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       "      <th>network_type</th>\n",
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       "      <td>1</td>\n",
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       "      <td>13.000</td>\n",
       "      <td>call</td>\n",
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       "      <td>Vodafone</td>\n",
       "      <td>mobile</td>\n",
       "      <td>06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>15/10/14 14:46</td>\n",
       "      <td>23.000</td>\n",
       "      <td>call</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>Meteor</td>\n",
       "      <td>mobile</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>15/10/14 14:48</td>\n",
       "      <td>4.000</td>\n",
       "      <td>call</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>Tesco</td>\n",
       "      <td>mobile</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>15/10/14 17:27</td>\n",
       "      <td>4.000</td>\n",
       "      <td>call</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>Tesco</td>\n",
       "      <td>mobile</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\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",
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>825</td>\n",
       "      <td>825</td>\n",
       "      <td>13/03/15 00:38</td>\n",
       "      <td>1.000</td>\n",
       "      <td>sms</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>world</td>\n",
       "      <td>world</td>\n",
       "      <td>00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>826</td>\n",
       "      <td>826</td>\n",
       "      <td>13/03/15 00:39</td>\n",
       "      <td>1.000</td>\n",
       "      <td>sms</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>Vodafone</td>\n",
       "      <td>mobile</td>\n",
       "      <td>00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>827</td>\n",
       "      <td>827</td>\n",
       "      <td>13/03/15 06:58</td>\n",
       "      <td>34.429</td>\n",
       "      <td>data</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>data</td>\n",
       "      <td>data</td>\n",
       "      <td>06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>828</td>\n",
       "      <td>828</td>\n",
       "      <td>14/03/15 00:13</td>\n",
       "      <td>1.000</td>\n",
       "      <td>sms</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>world</td>\n",
       "      <td>world</td>\n",
       "      <td>00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>829</td>\n",
       "      <td>829</td>\n",
       "      <td>14/03/15 00:16</td>\n",
       "      <td>1.000</td>\n",
       "      <td>sms</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>world</td>\n",
       "      <td>world</td>\n",
       "      <td>00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>830 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index            date  duration  item    month   network network_type  \\\n",
       "0        0  15/10/14 06:58    34.429  data  2014-11      data         data   \n",
       "1        1  15/10/14 06:58    13.000  call  2014-11  Vodafone       mobile   \n",
       "2        2  15/10/14 14:46    23.000  call  2014-11    Meteor       mobile   \n",
       "3        3  15/10/14 14:48     4.000  call  2014-11     Tesco       mobile   \n",
       "4        4  15/10/14 17:27     4.000  call  2014-11     Tesco       mobile   \n",
       "..     ...             ...       ...   ...      ...       ...          ...   \n",
       "825    825  13/03/15 00:38     1.000   sms  2015-03     world        world   \n",
       "826    826  13/03/15 00:39     1.000   sms  2015-03  Vodafone       mobile   \n",
       "827    827  13/03/15 06:58    34.429  data  2015-03      data         data   \n",
       "828    828  14/03/15 00:13     1.000   sms  2015-03     world        world   \n",
       "829    829  14/03/15 00:16     1.000   sms  2015-03     world        world   \n",
       "\n",
       "    hour  \n",
       "0     06  \n",
       "1     06  \n",
       "2     14  \n",
       "3     14  \n",
       "4     17  \n",
       "..   ...  \n",
       "825   00  \n",
       "826   00  \n",
       "827   06  \n",
       "828   00  \n",
       "829   00  \n",
       "\n",
       "[830 rows x 8 columns]"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# phone_df에서 'hour'컬럼에 시간대 삽입(apply사용)\n",
    "phone_df['hour'] = phone_df['date'].apply(lambda x: x[9:11])\n",
    "phone_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>date</th>\n",
       "      <th>duration</th>\n",
       "      <th>item</th>\n",
       "      <th>month</th>\n",
       "      <th>network</th>\n",
       "      <th>network_type</th>\n",
       "      <th>hour</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15/10/14 06:58</td>\n",
       "      <td>34.4</td>\n",
       "      <td>data</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>data</td>\n",
       "      <td>data</td>\n",
       "      <td>06</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>15/10/14 06:58</td>\n",
       "      <td>13.0</td>\n",
       "      <td>call</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>Vodafone</td>\n",
       "      <td>mobile</td>\n",
       "      <td>06</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>15/10/14 14:46</td>\n",
       "      <td>23.0</td>\n",
       "      <td>call</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>Meteor</td>\n",
       "      <td>mobile</td>\n",
       "      <td>14</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>15/10/14 14:48</td>\n",
       "      <td>4.0</td>\n",
       "      <td>call</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>Tesco</td>\n",
       "      <td>mobile</td>\n",
       "      <td>14</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>15/10/14 17:27</td>\n",
       "      <td>4.0</td>\n",
       "      <td>call</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>Tesco</td>\n",
       "      <td>mobile</td>\n",
       "      <td>17</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>825</td>\n",
       "      <td>825</td>\n",
       "      <td>13/03/15 00:38</td>\n",
       "      <td>1.0</td>\n",
       "      <td>sms</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>world</td>\n",
       "      <td>world</td>\n",
       "      <td>00</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>826</td>\n",
       "      <td>826</td>\n",
       "      <td>13/03/15 00:39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>sms</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>Vodafone</td>\n",
       "      <td>mobile</td>\n",
       "      <td>00</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>827</td>\n",
       "      <td>827</td>\n",
       "      <td>13/03/15 06:58</td>\n",
       "      <td>34.4</td>\n",
       "      <td>data</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>data</td>\n",
       "      <td>data</td>\n",
       "      <td>06</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>828</td>\n",
       "      <td>828</td>\n",
       "      <td>14/03/15 00:13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>sms</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>world</td>\n",
       "      <td>world</td>\n",
       "      <td>00</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>829</td>\n",
       "      <td>829</td>\n",
       "      <td>14/03/15 00:16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>sms</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>world</td>\n",
       "      <td>world</td>\n",
       "      <td>00</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>830 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index            date  duration  item    month   network network_type  \\\n",
       "0        0  15/10/14 06:58      34.4  data  2014-11      data         data   \n",
       "1        1  15/10/14 06:58      13.0  call  2014-11  Vodafone       mobile   \n",
       "2        2  15/10/14 14:46      23.0  call  2014-11    Meteor       mobile   \n",
       "3        3  15/10/14 14:48       4.0  call  2014-11     Tesco       mobile   \n",
       "4        4  15/10/14 17:27       4.0  call  2014-11     Tesco       mobile   \n",
       "..     ...             ...       ...   ...      ...       ...          ...   \n",
       "825    825  13/03/15 00:38       1.0   sms  2015-03     world        world   \n",
       "826    826  13/03/15 00:39       1.0   sms  2015-03  Vodafone       mobile   \n",
       "827    827  13/03/15 06:58      34.4  data  2015-03      data         data   \n",
       "828    828  14/03/15 00:13       1.0   sms  2015-03     world        world   \n",
       "829    829  14/03/15 00:16       1.0   sms  2015-03     world        world   \n",
       "\n",
       "    hour  year  \n",
       "0     06  2014  \n",
       "1     06  2014  \n",
       "2     14  2014  \n",
       "3     14  2014  \n",
       "4     17  2014  \n",
       "..   ...   ...  \n",
       "825   00  2015  \n",
       "826   00  2015  \n",
       "827   06  2015  \n",
       "828   00  2015  \n",
       "829   00  2015  \n",
       "\n",
       "[830 rows x 9 columns]"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 연도데이터를 year컬럼에 삽입하기\n",
    "def monthf(x):\n",
    "    return x.split('-')[0]\n",
    "# phone_df['year']=phone_df.month.apply(monthf)\n",
    "phone_df['year']=phone_df.month.apply(lambda d:d.split('-')[0])\n",
    "phone_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th>date</th>\n",
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       "      <td>2</td>\n",
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       "      <td>23.0</td>\n",
       "      <td>call</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>Meteor</td>\n",
       "      <td>mobile</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>15/10/14 14:48</td>\n",
       "      <td>4.0</td>\n",
       "      <td>call</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>Tesco</td>\n",
       "      <td>mobile</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>15/10/14 17:27</td>\n",
       "      <td>4.0</td>\n",
       "      <td>call</td>\n",
       "      <td>2014-11</td>\n",
       "      <td>Tesco</td>\n",
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       "      <td>825</td>\n",
       "      <td>13/03/15 00:38</td>\n",
       "      <td>1.0</td>\n",
       "      <td>sms</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>world</td>\n",
       "      <td>world</td>\n",
       "      <td>00</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>826</td>\n",
       "      <td>826</td>\n",
       "      <td>13/03/15 00:39</td>\n",
       "      <td>1.0</td>\n",
       "      <td>sms</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>Vodafone</td>\n",
       "      <td>mobile</td>\n",
       "      <td>00</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>827</td>\n",
       "      <td>827</td>\n",
       "      <td>13/03/15 06:58</td>\n",
       "      <td>34.4</td>\n",
       "      <td>data</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>data</td>\n",
       "      <td>data</td>\n",
       "      <td>06</td>\n",
       "      <td>15</td>\n",
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       "    <tr>\n",
       "      <td>828</td>\n",
       "      <td>828</td>\n",
       "      <td>14/03/15 00:13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>sms</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>world</td>\n",
       "      <td>world</td>\n",
       "      <td>00</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>829</td>\n",
       "      <td>829</td>\n",
       "      <td>14/03/15 00:16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>sms</td>\n",
       "      <td>2015-03</td>\n",
       "      <td>world</td>\n",
       "      <td>world</td>\n",
       "      <td>00</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>830 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index            date  duration  item    month   network network_type  \\\n",
       "0        0  15/10/14 06:58      34.4  data  2014-11      data         data   \n",
       "1        1  15/10/14 06:58      13.0  call  2014-11  Vodafone       mobile   \n",
       "2        2  15/10/14 14:46      23.0  call  2014-11    Meteor       mobile   \n",
       "3        3  15/10/14 14:48       4.0  call  2014-11     Tesco       mobile   \n",
       "4        4  15/10/14 17:27       4.0  call  2014-11     Tesco       mobile   \n",
       "..     ...             ...       ...   ...      ...       ...          ...   \n",
       "825    825  13/03/15 00:38       1.0   sms  2015-03     world        world   \n",
       "826    826  13/03/15 00:39       1.0   sms  2015-03  Vodafone       mobile   \n",
       "827    827  13/03/15 06:58      34.4  data  2015-03      data         data   \n",
       "828    828  14/03/15 00:13       1.0   sms  2015-03     world        world   \n",
       "829    829  14/03/15 00:16       1.0   sms  2015-03     world        world   \n",
       "\n",
       "    hour year  \n",
       "0     06   14  \n",
       "1     06   14  \n",
       "2     14   14  \n",
       "3     14   14  \n",
       "4     17   14  \n",
       "..   ...  ...  \n",
       "825   00   15  \n",
       "826   00   15  \n",
       "827   06   15  \n",
       "828   00   15  \n",
       "829   00   15  \n",
       "\n",
       "[830 rows x 9 columns]"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# duration값을 소숫점 첫째자리까지만\n",
    "phone_df['duration'] = phone_df['duration'].apply(lambda x:round(x,1))\n",
    "phone_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataFrame합치기\n",
    "##### merge(join과 유사) : 구조가 상이할 경우 유용\n",
    "##### concat(그냥 붙이기) :  구조가 일치할 경우 유용\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   subject_id  test_score\n",
      "0           1          51\n",
      "1           2          15\n",
      "2           3          15\n",
      "3           4          61\n",
      "4           5          16\n",
      "5           7          14\n",
      "6           8          15\n",
      "7           9          61\n",
      "8          10          16\n",
      "9          11          51\n",
      "10         12          16\n",
      "  subject_id first_name last_name\n",
      "0          4      Billy    Bonder\n",
      "1          5      Brian     Black\n",
      "2          6       Bran   Balwner\n",
      "3          7      Bryce     Brice\n",
      "4          8      Betty    Btisan\n"
     ]
    }
   ],
   "source": [
    "raw_data = {\n",
    "    'subject_id':[str(x) for x in range(1,13) if x!=6 ],\n",
    "    'test_score':[51,15,15,61,16,14,15,61,16,51,16]\n",
    "}\n",
    "df1 = pd.DataFrame(raw_data)\n",
    "print(df1)\n",
    "raw_data2 = {\n",
    "    'subject_id':['4','5','6','7','8'],\n",
    "    'first_name':['Billy', 'Brian', 'Bran', 'Bryce', 'Betty'],\n",
    "    'last_name':['Bonder', 'Black', 'Balwner', 'Brice','Btisan']\n",
    "}\n",
    "df2 = pd.DataFrame(raw_data2)\n",
    "print(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  subject_id  test_score first_name last_name\n",
      "0          4          61      Billy    Bonder\n",
      "1          5          16      Brian     Black\n",
      "2          7          14      Bryce     Brice\n",
      "3          8          15      Betty    Btisan\n",
      "   subject_id  test_score first_name last_name\n",
      "0           1        51.0        NaN       NaN\n",
      "1           2        15.0        NaN       NaN\n",
      "2           3        15.0        NaN       NaN\n",
      "3           4        61.0      Billy    Bonder\n",
      "4           5        16.0      Brian     Black\n",
      "5           7        14.0      Bryce     Brice\n",
      "6           8        15.0      Betty    Btisan\n",
      "7           9        61.0        NaN       NaN\n",
      "8          10        16.0        NaN       NaN\n",
      "9          11        51.0        NaN       NaN\n",
      "10         12        16.0        NaN       NaN\n",
      "11          6         NaN       Bran   Balwner\n",
      "   subject_id  test_score first_name last_name\n",
      "0           1          51        NaN       NaN\n",
      "1           2          15        NaN       NaN\n",
      "2           3          15        NaN       NaN\n",
      "3           4          61      Billy    Bonder\n",
      "4           5          16      Brian     Black\n",
      "5           7          14      Bryce     Brice\n",
      "6           8          15      Betty    Btisan\n",
      "7           9          61        NaN       NaN\n",
      "8          10          16        NaN       NaN\n",
      "9          11          51        NaN       NaN\n",
      "10         12          16        NaN       NaN\n",
      "  subject_id  test_score first_name last_name\n",
      "0          4        61.0      Billy    Bonder\n",
      "1          5        16.0      Brian     Black\n",
      "2          7        14.0      Bryce     Brice\n",
      "3          8        15.0      Betty    Btisan\n",
      "4          6         NaN       Bran   Balwner\n"
     ]
    }
   ],
   "source": [
    "print(pd.merge(df1,df2, on='subject_id', how='inner')) #내부조인\n",
    "print(pd.merge(df1,df2, on='subject_id', how='outer')) #외부조인\n",
    "print(pd.merge(df1,df2, on='subject_id', how='left')) #좌측조인\n",
    "print(pd.merge(df1,df2, on='subject_id', how='right')) #우측조인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   first_name last_name subject_id  test_score\n",
      "0         NaN       NaN          1        51.0\n",
      "1         NaN       NaN          2        15.0\n",
      "2         NaN       NaN          3        15.0\n",
      "3         NaN       NaN          4        61.0\n",
      "4         NaN       NaN          5        16.0\n",
      "5         NaN       NaN          7        14.0\n",
      "6         NaN       NaN          8        15.0\n",
      "7         NaN       NaN          9        61.0\n",
      "8         NaN       NaN         10        16.0\n",
      "9         NaN       NaN         11        51.0\n",
      "10        NaN       NaN         12        16.0\n",
      "0       Billy    Bonder          4         NaN\n",
      "1       Brian     Black          5         NaN\n",
      "2        Bran   Balwner          6         NaN\n",
      "3       Bryce     Brice          7         NaN\n",
      "4       Betty    Btisan          8         NaN\n",
      "   subject_id  test_score subject_id first_name last_name\n",
      "0           1          51          4      Billy    Bonder\n",
      "1           2          15          5      Brian     Black\n",
      "2           3          15          6       Bran   Balwner\n",
      "3           4          61          7      Bryce     Brice\n",
      "4           5          16          8      Betty    Btisan\n",
      "5           7          14        NaN        NaN       NaN\n",
      "6           8          15        NaN        NaN       NaN\n",
      "7           9          61        NaN        NaN       NaN\n",
      "8          10          16        NaN        NaN       NaN\n",
      "9          11          51        NaN        NaN       NaN\n",
      "10         12          16        NaN        NaN       NaN\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\103-04\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "print(pd.concat([df1,df2]))\n",
    "print(pd.concat([df1,df2], axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1', '2', '3', '4', '5', '7', '8', '9', '10', '11', '12']\n",
      "['1', '2', '3', '4', '5', '7', '8', '9', '10', '11', '12']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L =[str(x) for x in range(1,13)]\n",
    "L.remove('6')\n",
    "print(L)\n",
    "L = [str(x) for x in range(1,13) if x != 6]\n",
    "print(L)\n",
    "\n",
    "a = 3 if 5%2==0 else 4\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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