ohyun_ICT_2.ipynb

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      "name": "ohyun ICT 2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "CuEpx41WMMSZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 라이브러리를 임포트 해서 사용하겠다. as는  as뒤에 오는 이름으로 사용하겠다.\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8oit9k_SNdNL",
        "colab_type": "text"
      },
      "source": [
        "# 넘파이 실습\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uUUuhlGaMP4B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "a = [1,2,3,4,5,6]"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YzliaJ4aMkzE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "b = np.array(a)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P59KxW75M7Dg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "9e795386-9d85-4d1a-e9c0-54b4a7a5a84a"
      },
      "source": [
        "a"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2, 3, 4, 5, 6]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B8Ubh8GBM8Lo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "93e28af0-e361-4580-bf22-dbac9307b60b"
      },
      "source": [
        "b"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3, 4, 5, 6])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZfzH50WpM82Q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "74382471-0fac-4934-84ae-d8304080145b"
      },
      "source": [
        "type(a)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "list"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QjmzxvepM_8a",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "9d74618a-f206-434c-e045-97d446f49b23"
      },
      "source": [
        "type(b)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0nhRgAArNA9Y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "bf600a40-efea-4975-98ab-432e7f873475"
      },
      "source": [
        "b.reshape(2,3) # 모양바꾸기"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 2, 3],\n",
              "       [4, 5, 6]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XmONGXqnNImR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "4af309a5-ddcc-4b63-dcef-b995638f54b1"
      },
      "source": [
        "b.reshape(2,-1) #-1 은 자동으로 맞춰줌"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 2, 3],\n",
              "       [4, 5, 6]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jABCdhZxNl7j",
        "colab_type": "text"
      },
      "source": [
        "# 판다스 실습\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "28JUrvVYNOTp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ser1 = pd.Series((1,2,3,4),['오현고','대기고','일고','제주대'])"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qnS7Don1OLrw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BnvIHIHSOCvj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ser2 = pd.Series((1,2,3,4),['오현고','대기고','일고','한라대'])"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3uGqCyFyOQ_4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "outputId": "e11c9956-4f87-4d1e-90e8-71b7441d7776"
      },
      "source": [
        "ser1 + ser2 # pandas 는 계산시 float 형태로 변환 "
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "대기고    4.0\n",
              "오현고    2.0\n",
              "일고     6.0\n",
              "제주대    NaN\n",
              "한라대    NaN\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rjrWCTTjOS14",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "np.random.seed(10) # seed 는 랜덤한 값이 같게 나오게 해준다\n",
        "value = np.random.randn(5,4) # random.randn 랜덤하게 수를 만들어준다. 이때 5행 4열로 만든다는 의미이다."
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UTmYZQHNO44Y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "858a8d99-7bb5-43e1-a1b4-9b19210eafb2"
      },
      "source": [
        "df = pd.DataFrame(value,[1,2,3,4,5],['A','B','C','D']) # 처음값은 데이타 두번쨰값은 인덱스 세번쨰 값은 rame(value,[1,2,3,4,5],['A','B','C','D']) # 처음값은 데이타 두번쨰값은 인덱스 세번쨰 값은 컬럼이다\n",
        "df"
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      "execution_count": 23,
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              "      <td>0.108549</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.004291</td>\n",
              "      <td>-0.174600</td>\n",
              "      <td>0.433026</td>\n",
              "      <td>1.203037</td>\n",
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              "      <th>4</th>\n",
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              "      <td>0.228630</td>\n",
              "      <td>0.445138</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>-1.136602</td>\n",
              "      <td>0.135137</td>\n",
              "      <td>1.484537</td>\n",
              "      <td>-1.079805</td>\n",
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              "          A         B         C         D\n",
              "1  1.331587  0.715279 -1.545400 -0.008384\n",
              "2  0.621336 -0.720086  0.265512  0.108549\n",
              "3  0.004291 -0.174600  0.433026  1.203037\n",
              "4 -0.965066  1.028274  0.228630  0.445138\n",
              "5 -1.136602  0.135137  1.484537 -1.079805"
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        "outputId": "8e15db16-2e90-4384-8c68-b7a92f606869"
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      "source": [
        "df.iloc[0]"
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      "execution_count": 22,
      "outputs": [
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            "text/plain": [
              "A    1.331587\n",
              "B    0.715279\n",
              "C   -1.545400\n",
              "D   -0.008384\n",
              "Name: W, dtype: float64"
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          "execution_count": 22
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      "source": [
        "df > 0"
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      "execution_count": 24,
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              "      <td>False</td>\n",
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              "      <td>True</td>\n",
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              "      <td>True</td>\n",
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              "      <th>4</th>\n",
              "      <td>False</td>\n",
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              "      <td>True</td>\n",
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              "       A      B      C      D\n",
              "1   True   True  False  False\n",
              "2   True  False   True   True\n",
              "3   True  False   True   True\n",
              "4  False   True   True   True\n",
              "5  False   True   True  False"
            ]
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          "execution_count": 24
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          "height": 204
        },
        "outputId": "bad34f40-cea8-4b4b-8e4e-c34c1a1b4d6e"
      },
      "source": [
        "df.drop('D', axis = 1, inplace = True)\n",
        "df"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <th>1</th>\n",
              "      <td>1.331587</td>\n",
              "      <td>0.715279</td>\n",
              "      <td>-1.545400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.621336</td>\n",
              "      <td>-0.720086</td>\n",
              "      <td>0.265512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.004291</td>\n",
              "      <td>-0.174600</td>\n",
              "      <td>0.433026</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.965066</td>\n",
              "      <td>1.028274</td>\n",
              "      <td>0.228630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>-1.136602</td>\n",
              "      <td>0.135137</td>\n",
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              "          A         B         C\n",
              "1  1.331587  0.715279 -1.545400\n",
              "2  0.621336 -0.720086  0.265512\n",
              "3  0.004291 -0.174600  0.433026\n",
              "4 -0.965066  1.028274  0.228630\n",
              "5 -1.136602  0.135137  1.484537"
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          "execution_count": 25
        }
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    {
      "cell_type": "code",
      "metadata": {
        "id": "F8Iiec_TR4jA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['D'] = None"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aQwee5VTR7Y4",
        "colab_type": "code",
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      "source": [
        "df['D'] = 1"
      ],
      "execution_count": 30,
      "outputs": []
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      "metadata": {
        "id": "MqG8Y-hgSOM4",
        "colab_type": "code",
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      "source": [
        "a = [1,2,3,4,5]"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mrJly2fRSRmA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['D'] = a"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D9wgsr_cR_DA",
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        },
        "outputId": "e7c3db3e-2bf3-4669-a910-52805bf40700"
      },
      "source": [
        "df"
      ],
      "execution_count": 34,
      "outputs": [
        {
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.331587</td>\n",
              "      <td>0.715279</td>\n",
              "      <td>-1.545400</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.621336</td>\n",
              "      <td>-0.720086</td>\n",
              "      <td>0.265512</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.004291</td>\n",
              "      <td>-0.174600</td>\n",
              "      <td>0.433026</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.965066</td>\n",
              "      <td>1.028274</td>\n",
              "      <td>0.228630</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>-1.136602</td>\n",
              "      <td>0.135137</td>\n",
              "      <td>1.484537</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          A         B         C  D\n",
              "1  1.331587  0.715279 -1.545400  1\n",
              "2  0.621336 -0.720086  0.265512  2\n",
              "3  0.004291 -0.174600  0.433026  3\n",
              "4 -0.965066  1.028274  0.228630  4\n",
              "5 -1.136602  0.135137  1.484537  5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9E1C2aWQR_bo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = pd.DataFrame({'highSchool' : ['오현고','대기고','대기고','대기고','중앙고','오현고'],'person':['사람1','사람2','사람3','사람4','사람5','사람6']})"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OmoD8h-UTqNY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "02d21fa7-1e61-46aa-dde9-812f21683dbb"
      },
      "source": [
        "data"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>highSchool</th>\n",
              "      <th>person</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>오현고</td>\n",
              "      <td>사람1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>대기고</td>\n",
              "      <td>사람2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>대기고</td>\n",
              "      <td>사람3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>대기고</td>\n",
              "      <td>사람4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>중앙고</td>\n",
              "      <td>사람5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>오현고</td>\n",
              "      <td>사람6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  highSchool person\n",
              "0        오현고    사람1\n",
              "1        대기고    사람2\n",
              "2        대기고    사람3\n",
              "3        대기고    사람4\n",
              "4        중앙고    사람5\n",
              "5        오현고    사람6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YVMHUvq5TtLJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "4d98a537-cc4a-4b55-fd0e-30deb4faaa5c"
      },
      "source": [
        "data['number'] = 1\n",
        "data"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>highSchool</th>\n",
              "      <th>person</th>\n",
              "      <th>number</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>오현고</td>\n",
              "      <td>사람1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>대기고</td>\n",
              "      <td>사람2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>대기고</td>\n",
              "      <td>사람3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>대기고</td>\n",
              "      <td>사람4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>중앙고</td>\n",
              "      <td>사람5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>오현고</td>\n",
              "      <td>사람6</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  highSchool person  number\n",
              "0        오현고    사람1       1\n",
              "1        대기고    사람2       1\n",
              "2        대기고    사람3       1\n",
              "3        대기고    사람4       1\n",
              "4        중앙고    사람5       1\n",
              "5        오현고    사람6       1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O6bpqfWwT-Y4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7f661c53-3346-4312-fb9b-9ee3b241f87e"
      },
      "source": [
        "data.groupby('highSchool')"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7fef1e8d9860>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RxLeXMdAUVm3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 167
        },
        "outputId": "2e8940bd-95e6-47ee-c8c8-11a10bf03ad6"
      },
      "source": [
        "data.groupby('highSchool').sum() "
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>number</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>highSchool</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>대기고</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>오현고</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>중앙고</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            number\n",
              "highSchool        \n",
              "대기고              3\n",
              "오현고              2\n",
              "중앙고              1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kRMfumU7VGzh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "40d0197a-ff47-4940-f276-b156ea5a1333"
      },
      "source": [
        "data.groupby('highSchool').count()"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>person</th>\n",
              "      <th>number</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>highSchool</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>대기고</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>오현고</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>중앙고</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            person  number\n",
              "highSchool                \n",
              "대기고              3       3\n",
              "오현고              2       2\n",
              "중앙고              1       1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dbgDjwwjUaF4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "outputId": "b5ec471f-8b75-4a97-d138-1bac3adde36c"
      },
      "source": [
        "data.groupby('person').sum()"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>number</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>person</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>사람1</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>사람2</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>사람3</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>사람4</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>사람5</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>사람6</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        number\n",
              "person        \n",
              "사람1          1\n",
              "사람2          1\n",
              "사람3          1\n",
              "사람4          1\n",
              "사람5          1\n",
              "사람6          1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JQu9v0pPU-g_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "19e7b4e9-d0c7-4b68-d2ec-f7c15147a725"
      },
      "source": [
        "data.groupby('highSchool').std()"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>number</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>highSchool</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>대기고</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>오현고</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>중앙고</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            number\n",
              "highSchool        \n",
              "대기고            0.0\n",
              "오현고            0.0\n",
              "중앙고            NaN"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XduCvwCXVRmJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "outputId": "658a2c1d-a77d-4974-fd8d-47b7651d64c0"
      },
      "source": [
        "print(data.groupby(['highSchool','person']).sum() )\n",
        "print(data.groupby(['person','highSchool']).sum()) \n"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                   number\n",
            "highSchool person        \n",
            "대기고        사람2          1\n",
            "           사람3          1\n",
            "           사람4          1\n",
            "오현고        사람1          1\n",
            "           사람6          1\n",
            "중앙고        사람5          1\n",
            "                   number\n",
            "person highSchool        \n",
            "사람1    오현고              1\n",
            "사람2    대기고              1\n",
            "사람3    대기고              1\n",
            "사람4    대기고              1\n",
            "사람5    중앙고              1\n",
            "사람6    오현고              1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P82YN3XIV9zV",
        "colab_type": "text"
      },
      "source": [
        "# 그래프 MATPLOTLIB\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DItPA8FPVnTw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MNJfbRsIWKEP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6YC9rszaWO-R",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = np.linspace(0,5,11) #0 부터 5사이의 수를 11개를 만들겠다.\n",
        "y = x**2"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2zryQIPLW8W6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1cad46ff-c754-4659-c6be-20ae1e09cbe5"
      },
      "source": [
        "x"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CKKMPpNGW84h",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "b1c12412-79e4-478d-a656-8095180e3b2b"
      },
      "source": [
        "y"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.  ,  0.25,  1.  ,  2.25,  4.  ,  6.25,  9.  , 12.25, 16.  ,\n",
              "       20.25, 25.  ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uOtoE7AjW98y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "75575947-903b-4e15-f732-0c31c04d5e03"
      },
      "source": [
        "plt.plot(x,y)\n",
        "plt.show() # print 한거처럼 보임"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1DpgkRFXXHMZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "8e14e7a8-0f3b-49f7-fb3b-8f6fe29fb182"
      },
      "source": [
        "plt.plot(x,y)\n",
        "plt.xlabel('X label')\n",
        "plt.ylabel('Y label')\n",
        "plt.title('Title')\n",
        "plt.show()"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "co6DooJsXyiq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "1cc85b16-bb76-4acc-b83d-7f2512c66e4b"
      },
      "source": [
        "plt.subplot(2,2,1) # 2행 2열에 첫번째에 그림을 그리겠다.\n",
        "plt.plot(x,y,'r')\n",
        "plt.subplot(2,2,4) # 2행 2열에 네번재에 그림을 그리겠다.\n",
        "plt.plot(x,y,'r')\n",
        "plt.subplot(2,2,2) # 2행 2열에 두번째에 그림을 그리겠다.\n",
        "plt.plot(y,x,'--')"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fef1d3dcf28>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W-Fm8abgYSWJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "outputId": "18dc9231-f648-4233-aa6e-fc3dc899d0ac"
      },
      "source": [
        "fig = plt.figure()\n",
        "fig, axes = plt.subplots(nrows = 4, ncols = 1)\n",
        "axes[0].plot(x,y)\n",
        "axes[0].set_xlabel(\"X_label 1\")\n",
        "\n",
        "axes[1].plot(y,x)\n",
        "axes[1].set_xlabel(\"X_label 2\")\n",
        "\n",
        "axes[2].plot(x,y)\n",
        "axes[2].set_xlabel(\"X_label 3\")\n",
        "\n",
        "axes[3].plot(y,x)\n",
        "axes[3].set_xlabel(\"X_label 4\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xsqoE93Vc8X0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "1d4ae2fa-e2c7-45df-d065-c2fbaf81ff35"
      },
      "source": [
        "fig = plt.figure()\n",
        "axes1 = fig.add_axes([0.1,0.1,0.8,0.8]) # 바닥에서 0.1 만큼 위로 0.1만큼 오른쪽 80%만큼 가로가 줄어들고 80%만큼 세로가 줄어듬\n",
        "axes2 = fig.add_axes([0.2,0.4,0.4,0.2])\n",
        "plt.show()"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mDw6fCbZedCE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}