ch04 Numpy, Pandas basic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "07c60d3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Numpy 객체\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c049f79a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#배열 생성\n",
    "np.array([1,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "84c41365",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([[1,2],[3,4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "310e490c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([[1,2],\n",
    "          [3,4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b464bc7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([1,2,3,4]).reshape([2,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c2ac67ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#색인\n",
    "array = np.array([[1,2,3], [4,5,6]])\n",
    "array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2b57d099",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "16aa95b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 5, 6])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a13688fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array[1][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e7e1be03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array[1,1] # row index, col index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a594549d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#매서드, 어트리뷰트\n",
    "array.ndim   #몇 차원이냐?,  n = number of 라는 뜻"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d591b080",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array.shape  #각 차원의 길이"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a13ab9b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array.sum()  #합"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "47094cc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.707825127659933"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array.std()  #표준편차"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "31760d3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 2, 3])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#다양한 함수\n",
    "nums = np.array([1, 2, 2, 3])\n",
    "nums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9e156d72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 2, 3}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(nums) #집함 (중복 제외)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0ce9471a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(nums) #길이"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "568c0bb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(nums) # 어레이, 중복 제거"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c83c832c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5, 10, 15])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(start = 5, stop = 20, step = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7ab0c4ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['no!', 'ok!', 'ok!', 'no!'], dtype='<U3')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(nums == 2, \"ok!\", \"no!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "42ed59b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Pandas 객체\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "56f80a7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser = pd.Series([1, 2, 3])\n",
    "ser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f879648d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3], dtype=int64)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "78949e0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=3, step=1)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cc86ddd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "3    3\n",
       "4    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#시리즈의 생성 및 메서드\n",
    "pd.Series([1,2,3,3,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "67ea1706",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([1,2,3], index = [\"a\", \"b\", \"c\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "69c51430",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.sum()  # 합계"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "94049ba6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.min()  # 최소값"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7b5dfc2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.max()  # 최대값"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "664c4b97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.mean()  # 평균"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b6162d0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.std()  # 표준편차"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "351926d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.skew()  # 왜도"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "dacbd36f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.kurt()  # 첨도"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "13cabe47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3], dtype=int64)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.unique()  # 고유값"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "95c28927",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.idxmax()  # 최대값의 위치"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "792ef9f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.idxmin()  # 최소값의 위치"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "cf010799",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1     True\n",
       "2    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.isin([1,2])  # 포함 여부 검사"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7b0b177c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.isin([1,2]).sum()  # 1, 2가 몇개 있느냐?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1f52e9ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "True + True + False # True = 1, False = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "19f3395d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#색인\n",
    "ser[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "d1082909",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    2\n",
       "2    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser[[1,2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f10fd996",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    2\n",
       "2    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e8cdd5bc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "298cf38f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "afb34684",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0\n",
       "3  1\n",
       "3  2\n",
       "4  2"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#데이터프레임 개요\n",
    "pd.DataFrame([1,2,2],[3,3,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "282230eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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    "pd.DataFrame({\"aa\":[1,2,3],\"bb\":[2,3,4]})"
   ]
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   "cell_type": "code",
   "execution_count": 58,
   "id": "2dc26951",
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   "id": "edc64731",
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    "df = pd.DataFrame({\"aa\":[1,2,3],\n",
    "                   \"bb\":[2,3,4]})\n",
    "df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "0781fb25",
   "metadata": {},
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    {
     "data": {
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       "0    1\n",
       "1    2\n",
       "2    3\n",
       "Name: aa, dtype: int64"
      ]
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     "execution_count": 70,
     "metadata": {},
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    "df.aa"
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   "cell_type": "code",
   "execution_count": 62,
   "id": "95d3e6f3",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "Name: aa, dtype: int64"
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     "execution_count": 62,
     "metadata": {},
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    "df[\"aa\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "74c5699f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "0    1\n",
       "1    2\n",
       "Name: aa, dtype: int64"
      ]
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     "execution_count": 63,
     "metadata": {},
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   ]
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  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "b78c3a09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "Name: aa, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.iloc[:,0]  # 정수 기반 인덱싱"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "5ffe910e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "Name: aa, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
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    "df.loc[:, \"aa\"] # 문자 기반 인덱싱"
   ]
  },
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   "cell_type": "code",
   "execution_count": 66,
   "id": "599a1bdb",
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   "id": "bc6c15ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "Name: aa, dtype: int64"
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     "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "85ea0992",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "Name: aa, dtype: int64"
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24d6bcf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 요약\n",
    "\"\"\"\n",
    "crosstab (빈도표)\n",
    "두 변수를 조합하여 살펴볼 때 사용하는 Pandas 함수\n",
    "normalize 인자에 True, 1, 0 값을 할당하여 값을 정규화 할 수 있음\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "4d53b59f",
   "metadata": {},
   "outputs": [
    {
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       "bb  2  3  4\n",
       "aa         \n",
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       "2   0  1  0\n",
       "3   0  0  1"
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     "execution_count": 71,
     "metadata": {},
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   "source": [
    "pd.crosstab(df[\"aa\"], df[\"bb\"])"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b68194a0",
   "metadata": {},
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   "source": [
    "\"\"\"\n",
    "groupby\n",
    "특정 변수를 기준으로 요약 연산을 할 때 활용하는 데이터프레임의 메서드\n",
    "두 개 이상의 변수를 기준으로 연산할 경우 변수명을 리스트로 묶음\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "28f282aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "aa\n",
       "1    2\n",
       "2    3\n",
       "3    4\n",
       "Name: bb, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"aa\")[\"bb\"].sum()"
   ]
  }
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