material/library/practice/library_평가문제_풀이.ipynb

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  "metadata": {
    "colab": {
      "name": "library_평가문제_풀이.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NaBajNXcrkvS"
      },
      "source": [
        "# 사전 준비 사항\n",
        "\n",
        "아래를 실행시켜서 준비하고 이후 퀴즈를 풀어주세요."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cYeD_ZZTpNXJ",
        "outputId": "b6bc73d1-e0d7-48e3-9dd8-796fb529cb34"
      },
      "source": [
        "%%writefile data.csv\n",
        "name,age,tip\n",
        "tom,33,10.5\n",
        "jerry,35,8\n",
        "철수,25,3.5\n",
        "영희,26,8.8\n",
        "brute,22,0\n",
        "hank,31,6.6"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Overwriting data.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "noud8vNFrxPY",
        "outputId": "12820c18-97fc-4855-cbe6-c0cd588cb6f5"
      },
      "source": [
        "%%writefile flawed_data.csv\n",
        "age,tip\n",
        "33,10.5\n",
        ",8\n",
        "25,-3.5\n",
        "26,\n",
        "22,0\n",
        "31,6.6"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Overwriting flawed_data.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z5sMI708zOlk",
        "outputId": "dfe0b9ff-6eac-456b-de96-c914712d81db"
      },
      "source": [
        "%%writefile tips.csv\n",
        "total_bill,tip,sex,smoker,day,time,size\n",
        "16.99,1.01,Female,No,Sun,Dinner,2\n",
        "10.34,1.66,Male,No,Sun,Dinner,3\n",
        "21.01,3.5,Male,No,Sun,Dinner,3\n",
        "23.68,3.31,Male,No,Sun,Dinner,2\n",
        "24.59,3.61,Female,No,Sun,Dinner,4\n",
        "25.29,4.71,Male,No,Sun,Dinner,4\n",
        "8.77,2.0,Male,No,Sun,Dinner,2\n",
        "26.88,3.12,Male,No,Sun,Dinner,4\n",
        "15.04,1.96,Male,No,Sun,Dinner,2\n",
        "14.78,3.23,Male,No,Sun,Dinner,2"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Overwriting tips.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3SLTKKwWOIgu"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O9gFh1NYo1gc"
      },
      "source": [
        "# Quiz.1\n",
        "\n",
        "파일 data.csv의 내용은 다음과 같다.\n",
        "```\n",
        "name,age,tip\n",
        "tom,33,10.5\n",
        "jerry,35,8\n",
        "철수,25,3.5\n",
        "영희,26,8.8\n",
        "brute,22,0\n",
        "hank,31,6.6\n",
        "```\n",
        "\n",
        "data.csv를 Pandas로 읽고,\n",
        "\n",
        "tip 컬럼의 값을 2배한 값으로 하는 새로운 컬럼 doubled_tip을 생성하고 dataframe을 출력한다. \n",
        "\n",
        "기대되는 출력은 다음과 같다.\n",
        "```\n",
        "    name  age   tip  doubled_tip\n",
        "0    tom   33  10.5         21.0\n",
        "1  jerry   35   8.0         16.0\n",
        "2     철수   25   3.5          7.0\n",
        "3     영희   26   8.8         17.6\n",
        "4  brute   22   0.0          0.0\n",
        "5   hank   31   6.6         13.2\n",
        "```\n",
        "\n",
        "age 컬럼의 값이 30보다 큰 row만 추린 dataframe을 구하고 이를 출력한다.\n",
        "\n",
        "기대되는 출력은 다음과 같다.\n",
        "```\n",
        "2       철수\n",
        "3       영희\n",
        "4    brute\n",
        "Name: name, dtype: object\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FJaltqQGu3N0",
        "outputId": "8a45c6ee-83e2-491b-c4a8-24b162a31a38"
      },
      "source": [
        "df = pd.read_csv(\"data.csv\")\n",
        "print(df)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "    name  age   tip\n",
            "0    tom   33  10.5\n",
            "1  jerry   35   8.0\n",
            "2     철수   25   3.5\n",
            "3     영희   26   8.8\n",
            "4  brute   22   0.0\n",
            "5   hank   31   6.6\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iigcJ_-Ou7aA",
        "outputId": "3f72dafa-0ec2-44ee-e27e-96ae0930461a"
      },
      "source": [
        "df['doubled_tip'] = df.tip * 2.0\n",
        "print(df)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "    name  age   tip  doubled_tip\n",
            "0    tom   33  10.5         21.0\n",
            "1  jerry   35   8.0         16.0\n",
            "2     철수   25   3.5          7.0\n",
            "3     영희   26   8.8         17.6\n",
            "4  brute   22   0.0          0.0\n",
            "5   hank   31   6.6         13.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IS8qJv3mu_fP",
        "outputId": "41e335f7-cf83-405e-be6b-eaca87ae62fc"
      },
      "source": [
        "print(df.loc[df.age>30, \"name\"])"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0      tom\n",
            "1    jerry\n",
            "5     hank\n",
            "Name: name, dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yOUdeT8mo7IM"
      },
      "source": [
        "# Quiz.2\n",
        "\n",
        "파일 flawed_data.csv의 내용은 다음과 같다.\n",
        "```\n",
        "age,tip\n",
        "33,10.5\n",
        ",8\n",
        "25,-3.5\n",
        "26,\n",
        "22,0\n",
        "31,6.6\n",
        "```\n",
        "\n",
        "Pandas로 읽고,\n",
        "\n",
        "tip 컬럼의 음수 값은 0으로 수정한다.\n",
        "\n",
        "그리고 결측치의 값을 각 컬럼의 평균값으로 채우고 data frame을 출력한다.\n",
        "\n",
        "기대되는 출력은 다음과 같다.\n",
        "```\n",
        "    age    tip\n",
        "0  33.0  10.50\n",
        "1  27.4   8.00\n",
        "2  25.0   0.00\n",
        "3  26.0   5.02\n",
        "4  22.0   0.00\n",
        "5  31.0   6.60\n",
        "```\n",
        "\n",
        "그리고 age, tip 컬럼의 값을 0.0 ~ 1.0의 값으로 normalization한 age, tip을 출력한다.\n",
        "\n",
        "기대되는 출력은 다음과 같다.\n",
        "```\n",
        "        age       tip\n",
        "0  1.000000  1.000000\n",
        "1  0.490909  0.761905\n",
        "2  0.272727  0.000000\n",
        "3  0.363636  0.478095\n",
        "4  0.000000  0.000000\n",
        "5  0.818182  0.628571\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g9VrBfYqsn_F",
        "outputId": "b6ac3023-10db-42ce-c9e3-ca22cd399b26"
      },
      "source": [
        "df = pd.read_csv(\"flawed_data.csv\")\n",
        "print(df)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "    age   tip\n",
            "0  33.0  10.5\n",
            "1   NaN   8.0\n",
            "2  25.0  -3.5\n",
            "3  26.0   NaN\n",
            "4  22.0   0.0\n",
            "5  31.0   6.6\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        },
        "id": "iL-tPCnduPp0",
        "outputId": "11d2dcc3-aa0b-4b7d-f226-9553cb40c937"
      },
      "source": [
        "df.loc[df.tip<0, \"tip\"] = 0\n",
        "df"
      ],
      "execution_count": 14,
      "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>age</th>\n",
              "      <th>tip</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>33.0</td>\n",
              "      <td>10.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>25.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>26.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>22.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>31.0</td>\n",
              "      <td>6.6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    age   tip\n",
              "0  33.0  10.5\n",
              "1   NaN   8.0\n",
              "2  25.0   0.0\n",
              "3  26.0   NaN\n",
              "4  22.0   0.0\n",
              "5  31.0   6.6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZWEGZVimssSV",
        "outputId": "4cc9a261-92f4-4da8-e60f-44236e436843"
      },
      "source": [
        "df.fillna(df.mean(), inplace=True)\n",
        "print(df)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "    age    tip\n",
            "0  33.0  10.50\n",
            "1  27.4   8.00\n",
            "2  25.0   0.00\n",
            "3  26.0   5.02\n",
            "4  22.0   0.00\n",
            "5  31.0   6.60\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r2JAn3g7ssPW",
        "outputId": "f0513709-7560-4362-dd44-924c241a5e9b"
      },
      "source": [
        "df = (df - df.min()) / (df.max() - df.min())\n",
        "print(df)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "        age       tip\n",
            "0  1.000000  1.000000\n",
            "1  0.490909  0.761905\n",
            "2  0.272727  0.000000\n",
            "3  0.363636  0.478095\n",
            "4  0.000000  0.000000\n",
            "5  0.818182  0.628571\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rPVhPYF0ssMW"
      },
      "source": [
        ""
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HpiwR1T8o9x3"
      },
      "source": [
        "# Quiz.3\n",
        "\n",
        "다음과 같은 실수 1~15의 값을 갖는 (3,5) 형태의 numpy array를 생성하고\n",
        "```\n",
        "[[ 1.  2.  3.  4.  5.]\n",
        " [ 6.  7.  8.  9. 10.]\n",
        " [11. 12. 13. 14. 15.]]\n",
        "```\n",
        "\n",
        "각 열 별로 합을 구한 5개 요소를 같는 numpy array를 출력한다.\n",
        "기대되는 출력은 다음과 같다.\n",
        "```\n",
        "[18. 21. 24. 27. 30.]\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VE_cPZIivl5l",
        "outputId": "24a022ea-a0d6-468d-bb3c-d684e158644d"
      },
      "source": [
        "a = np.arange(1,16).reshape(3,5)\n",
        "a = a.astype(float)\n",
        "print(a)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 1.  2.  3.  4.  5.]\n",
            " [ 6.  7.  8.  9. 10.]\n",
            " [11. 12. 13. 14. 15.]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T3WVWnaQvrbh",
        "outputId": "4e147477-2599-4d22-f461-f459c56cc49b"
      },
      "source": [
        "s = np.sum(a, axis=0)\n",
        "print(s)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[18. 21. 24. 27. 30.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yRJx-7UFpAI-"
      },
      "source": [
        "# Quiz.4\n",
        "\n",
        "다음과 같은 numpy array를 생성하여 출력하고\n",
        "```\n",
        "[[ 0  1  2  3  4]\n",
        " [ 5  6  7  8  9]\n",
        " [10 11 12 13 14]\n",
        " [15 16 17 18 19]\n",
        " [20 21 22 23 24]]\n",
        "```\n",
        "\n",
        "이를 슬라이싱 해서 다음과 같은 numpy array를 출력한다.\n",
        "```\n",
        "[[ 7  8  9]\n",
        " [12 13 14]\n",
        " [17 18 19]]\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7ZO3BbI4vrX9",
        "outputId": "5f879a03-7e6c-4de0-de24-d2cc1441227f"
      },
      "source": [
        "a = np.arange(25).reshape(5,5)\n",
        "print(a)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 0  1  2  3  4]\n",
            " [ 5  6  7  8  9]\n",
            " [10 11 12 13 14]\n",
            " [15 16 17 18 19]\n",
            " [20 21 22 23 24]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ltcEI_01vrUf",
        "outputId": "25d4c43c-9fa4-4bbc-9550-4af5b76fada0"
      },
      "source": [
        "a = a[1:4, 2:]\n",
        "print(a)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 7  8  9]\n",
            " [12 13 14]\n",
            " [17 18 19]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aTdxUCfwvrQn"
      },
      "source": [
        ""
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Og91ZtXO242a"
      },
      "source": [
        "## Quiz.5\n",
        "\n",
        "다음과 같이 좌상단엔 x^2그래프, 우하단엔 2x+1그래프를 그린다.\n",
        "\n",
        "![대체 텍스트](https://github.com/dhrim/hongik_2021/raw/master/material/library/images/graph.png)\n",
        "\n",
        "\n",
        "둘다 x는 -1 ~ 1까지 0.1간격이다.\n",
        "\n",
        "<br>\n",
        "\n",
        "색깔, 크기, 축이름, 축 눈금, 그래프 이름 등등의 가타 설정은 임의로 해도 된다.\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "X97WpBbB3LmD",
        "outputId": "2bc337c1-6eaa-4833-c613-685de46d3c73"
      },
      "source": [
        "x = np.arange(-1,1.001,0.1)\n",
        "\n",
        "plt.subplot(221)\n",
        "plt.scatter(x, x**2)\n",
        "plt.subplot(224)\n",
        "plt.scatter(x, x*2+1)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 21,
      "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": "markdown",
      "metadata": {
        "id": "1VoDE0y5pCCK"
      },
      "source": [
        "# Quiz.6\n",
        "\n",
        "tips.csv 파일을 읽어 다음과 같은 histogram 그래프를 그린다.\n",
        "\n",
        "![대체 텍스트](https://github.com/dhrim/hongik_2021/raw/master/material/library/images/tips.png)\n",
        "\n",
        "\n",
        "\n",
        "1~2값은 여자 1개, 남자 3개이고 \n",
        "\n",
        "3~4 구간은 1개, 4개이고, \n",
        "\n",
        "4~5 구간은 0개, 1개로 그려져야 한다.\n",
        "\n",
        "<br>\n",
        "힌트 : 이때 히스토그램 bin의 폭을 1로 설정한다.\n",
        "\n",
        "색깔, 크기, 축이름, 축 눈금, 그래프 이름 등등의 가타 설정은 임의로 해도 된다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O1AAlMWWy67T",
        "outputId": "9cb38651-41ff-49c4-c4ad-2e0b279c1a75"
      },
      "source": [
        "tips = pd.read_csv(\"tips.csv\")\n",
        "print(tips)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   total_bill   tip     sex smoker  day    time  size\n",
            "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
            "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
            "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
            "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
            "4       24.59  3.61  Female     No  Sun  Dinner     4\n",
            "5       25.29  4.71    Male     No  Sun  Dinner     4\n",
            "6        8.77  2.00    Male     No  Sun  Dinner     2\n",
            "7       26.88  3.12    Male     No  Sun  Dinner     4\n",
            "8       15.04  1.96    Male     No  Sun  Dinner     2\n",
            "9       14.78  3.23    Male     No  Sun  Dinner     2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "deZxvH7yzBKP",
        "outputId": "b37363c7-611d-4d75-adce-560f7fb4d8a1"
      },
      "source": [
        "sns.histplot(data=tips, x=\"tip\", hue=\"sex\", alpha=.3, shrink=.8, multiple=\"dodge\", binwidth=1)\n",
        "plt.show()"
      ],
      "execution_count": 23,
      "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": "93ECfQp3znGo"
      },
      "source": [
        ""
      ],
      "execution_count": 23,
      "outputs": []
    }
  ]
}