13.분석 모듈 Preview.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "91d22388",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "194da2ea",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40</td>\n",
       "      <td>50</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0   1   2\n",
       "0  10  20  30\n",
       "1  40  50  60\n",
       "2  70  80  90"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DataFrame (excel처럼 생김)\n",
    "# List로 DataFrame 만들기\n",
    "df = pd.DataFrame([\n",
    "    [10, 20, 30],\n",
    "    [40, 50, 60],\n",
    "    [70, 80, 90]\n",
    "])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "efc48c43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10\n",
       "1    40\n",
       "2    70\n",
       "Name: 0, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 열 기준으로 추출하기\n",
    "df.loc[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9d081e03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10\n",
       "1    20\n",
       "2    30\n",
       "Name: 0, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 행 기준으로 추출하기\n",
    "df.loc[0, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "09367aa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10\n",
       "1    20\n",
       "Name: 0, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[0, 0:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a3ca17eb",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0   1\n",
       "0  10  20\n",
       "1  40  50\n",
       "2  70  80"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, range(0,2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a27041e9",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40</td>\n",
       "      <td>50</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0   1   2\n",
       "0  10  20  30\n",
       "1  40  50  60"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[0:1, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c15b06c9",
   "metadata": {},
   "source": [
    "# Dictionary로 DataFrame 만들기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "506cfef0",
   "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>weight</th>\n",
       "      <th>height</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>80.0</td>\n",
       "      <td>170</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>70.4</td>\n",
       "      <td>180</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65.5</td>\n",
       "      <td>155</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45.9</td>\n",
       "      <td>143</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51.2</td>\n",
       "      <td>154</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   weight  height type\n",
       "0    80.0     170    f\n",
       "1    70.4     180    n\n",
       "2    65.5     155    n\n",
       "3    45.9     143    t\n",
       "4    51.2     154    t"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tbl = pd.DataFrame({\n",
    "    \"weight\" : [80.0, 70.4, 65.5, 45.9, 51.2],\n",
    "    \"height\" : [170, 180, 155, 143, 154],\n",
    "    \"type\" : ['f', 'n', 'n', 't', 't']\n",
    "})\n",
    "tbl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6c7c3004",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    80.0\n",
       "1    70.4\n",
       "2    65.5\n",
       "3    45.9\n",
       "4    51.2\n",
       "Name: weight, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 몸무게 컬럼만 출력하기\n",
    "tbl.loc[:, 'weight']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "05ce6ac9",
   "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>weight</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>80.0</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>70.4</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65.5</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45.9</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51.2</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   weight  height\n",
       "0    80.0     170\n",
       "1    70.4     180\n",
       "2    65.5     155\n",
       "3    45.9     143\n",
       "4    51.2     154"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 몸무게 컬럼과 키 컬럼 출력하기\n",
    "tbl.loc[:, ['weight','height']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d569ddf1",
   "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>weight</th>\n",
       "      <th>height</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45.9</td>\n",
       "      <td>143</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51.2</td>\n",
       "      <td>154</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65.5</td>\n",
       "      <td>155</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>80.0</td>\n",
       "      <td>170</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>70.4</td>\n",
       "      <td>180</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   weight  height type\n",
       "3    45.9     143    t\n",
       "4    51.2     154    t\n",
       "2    65.5     155    n\n",
       "0    80.0     170    f\n",
       "1    70.4     180    n"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 정렬의 오름차순과 내림차순\n",
    "# height 기준으로 오름차순\n",
    "tbl.sort_values(by='height') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "be8d10b0",
   "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>weight</th>\n",
       "      <th>height</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>70.4</td>\n",
       "      <td>180</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>80.0</td>\n",
       "      <td>170</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65.5</td>\n",
       "      <td>155</td>\n",
       "      <td>n</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51.2</td>\n",
       "      <td>154</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>45.9</td>\n",
       "      <td>143</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   weight  height type\n",
       "1    70.4     180    n\n",
       "0    80.0     170    f\n",
       "2    65.5     155    n\n",
       "4    51.2     154    t\n",
       "3    45.9     143    t"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# height 기준으로 내림차순\n",
    "tbl.sort_values(by='height', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd694d43",
   "metadata": {},
   "source": [
    "# Pandas와 MatplotLib을 이용하여 시각화 하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2a69f494",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pylab as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "510bbbd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 불러오기\n",
    "names = pd.read_csv(\"./Data/Baby_Names_1880-2014.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dacdbec4",
   "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>name</th>\n",
       "      <th>gender</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mary</td>\n",
       "      <td>F</td>\n",
       "      <td>7065</td>\n",
       "      <td>1880</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anna</td>\n",
       "      <td>F</td>\n",
       "      <td>2604</td>\n",
       "      <td>1880</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Emma</td>\n",
       "      <td>F</td>\n",
       "      <td>2003</td>\n",
       "      <td>1880</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Elizabeth</td>\n",
       "      <td>F</td>\n",
       "      <td>1939</td>\n",
       "      <td>1880</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Minnie</td>\n",
       "      <td>F</td>\n",
       "      <td>1746</td>\n",
       "      <td>1880</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        name gender  births  year  id\n",
       "0       Mary      F    7065  1880   1\n",
       "1       Anna      F    2604  1880   2\n",
       "2       Emma      F    2003  1880   3\n",
       "3  Elizabeth      F    1939  1880   4\n",
       "4     Minnie      F    1746  1880   5"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 데이터를 앞에서 5개만 불러오기(데이터를 잘 불러왔는지 확인하기 위함)\n",
    "names.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "246c1911",
   "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>name</th>\n",
       "      <th>gender</th>\n",
       "      <th>births</th>\n",
       "      <th>year</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1825383</th>\n",
       "      <td>Zykeem</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "      <td>13973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1825384</th>\n",
       "      <td>Zymeer</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "      <td>13974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1825385</th>\n",
       "      <td>Zymiere</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "      <td>13975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1825386</th>\n",
       "      <td>Zyran</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "      <td>13976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1825387</th>\n",
       "      <td>Zyrin</td>\n",
       "      <td>M</td>\n",
       "      <td>5</td>\n",
       "      <td>2014</td>\n",
       "      <td>13977</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            name gender  births  year     id\n",
       "1825383   Zykeem      M       5  2014  13973\n",
       "1825384   Zymeer      M       5  2014  13974\n",
       "1825385  Zymiere      M       5  2014  13975\n",
       "1825386    Zyran      M       5  2014  13976\n",
       "1825387    Zyrin      M       5  2014  13977"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 데이터를 뒤에서 5개만 불러오기\n",
    "names.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e545d53f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name      1825388\n",
       "gender    1825388\n",
       "births    1825388\n",
       "year      1825388\n",
       "id        1825388\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 전체 갯수\n",
    "names.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c727de64",
   "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>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>90993</td>\n",
       "      <td>110491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1881</th>\n",
       "      <td>91954</td>\n",
       "      <td>100745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>107850</td>\n",
       "      <td>113683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>112321</td>\n",
       "      <td>104629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>129022</td>\n",
       "      <td>114445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1885</th>\n",
       "      <td>133055</td>\n",
       "      <td>107800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1886</th>\n",
       "      <td>144535</td>\n",
       "      <td>110784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1887</th>\n",
       "      <td>145982</td>\n",
       "      <td>101414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1888</th>\n",
       "      <td>178627</td>\n",
       "      <td>120853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1889</th>\n",
       "      <td>178366</td>\n",
       "      <td>110584</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender       F       M\n",
       "year                  \n",
       "1880     90993  110491\n",
       "1881     91954  100745\n",
       "1882    107850  113683\n",
       "1883    112321  104629\n",
       "1884    129022  114445\n",
       "1885    133055  107800\n",
       "1886    144535  110784\n",
       "1887    145982  101414\n",
       "1888    178627  120853\n",
       "1889    178366  110584"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pivot을 이용하여 각 이름의 count를 년도별 합계로 그룹화 하여 집계하기\n",
    "total_births = names.pivot_table('births', index='year', columns='gender', aggfunc=sum)\n",
    "total_births.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f59da0ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='year'>"
      ]
     },
     "execution_count": 27,
     "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": [
    "# matplotlib을 이용하여 그래프 생성하기\n",
    "total_births.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "747fad92",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 그래프의 가로 세로 비율 적용\n",
    "plt.rcParams['figure.figsize'] = (14, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9143d245",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Total births by gender and year'}, xlabel='year'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 제목 붙이기\n",
    "total_births.plot(title = 'Total births by gender and year')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9cd24ff3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc878529",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "773056e0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f6d2a58",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ffba7eb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}