두잇_datetime_ebola_banks_assignment.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/wlsisl/ESAA_22-2/blob/main/ebola_banks_assignment.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "####두잇 판다스 p.243~255\n",
        "### datetime 오브젝트로 변환하기 - to_datetime메서드\n",
        "\n",
        "경우에 따라서 시계열 데이터를 문자열로 저장해야할 때가 있다. \n",
        "\n",
        "하지만 문자열은 시간계산을 할 수 없어서 datetime오브젝트로 변환해 주어야한다.\n",
        "\n",
        "<문자열을 datetime오브젝트로 변환하기>\n",
        "\n",
        "1.ebola 데이터 집합 불러오기"
      ],
      "metadata": {
        "id": "UfdG0O3XDbxF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cDncEwE5Ez9q",
        "outputId": "dbc5c9ce-17cc-4b21-f6ac-a3dc04b823d0"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from datetime import datetime\n",
        "import os\n",
        "\n",
        "ebola = pd.read_csv('/content/drive/MyDrive/country_timeseries.csv')"
      ],
      "metadata": {
        "id": "_COvgMpuDrme"
      },
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. 데이터 프레임 확인하기 . 문자열로 저장된 Date열이 있다."
      ],
      "metadata": {
        "id": "FiGWVK5iKnj8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "print(ebola.info())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pqQLwuwUKdpr",
        "outputId": "3d9147b4-6a96-412f-e541-159b024e4849"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 122 entries, 0 to 121\n",
            "Data columns (total 18 columns):\n",
            " #   Column               Non-Null Count  Dtype  \n",
            "---  ------               --------------  -----  \n",
            " 0   Date                 122 non-null    object \n",
            " 1   Day                  122 non-null    int64  \n",
            " 2   Cases_Guinea         93 non-null     float64\n",
            " 3   Cases_Liberia        83 non-null     float64\n",
            " 4   Cases_SierraLeone    87 non-null     float64\n",
            " 5   Cases_Nigeria        38 non-null     float64\n",
            " 6   Cases_Senegal        25 non-null     float64\n",
            " 7   Cases_UnitedStates   18 non-null     float64\n",
            " 8   Cases_Spain          16 non-null     float64\n",
            " 9   Cases_Mali           12 non-null     float64\n",
            " 10  Deaths_Guinea        92 non-null     float64\n",
            " 11  Deaths_Liberia       81 non-null     float64\n",
            " 12  Deaths_SierraLeone   87 non-null     float64\n",
            " 13  Deaths_Nigeria       38 non-null     float64\n",
            " 14  Deaths_Senegal       22 non-null     float64\n",
            " 15  Deaths_UnitedStates  18 non-null     float64\n",
            " 16  Deaths_Spain         16 non-null     float64\n",
            " 17  Deaths_Mali          12 non-null     float64\n",
            "dtypes: float64(16), int64(1), object(1)\n",
            "memory usage: 17.3+ KB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "3. to_datetime 메서드를 사용하여 Date열의 자료형을 datetime 오브젝트로 변환하기\n",
        "\n",
        "- 변환한 다음 새로운 열에 추가하기"
      ],
      "metadata": {
        "id": "pDF1jaigKltJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ebola['date_dt']=pd.to_datetime(ebola['Date'])\n",
        "print(ebola.info())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aXXjRjdGKkVl",
        "outputId": "48bd3f27-d3ec-485d-962c-36f9e19a65dd"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 122 entries, 0 to 121\n",
            "Data columns (total 19 columns):\n",
            " #   Column               Non-Null Count  Dtype         \n",
            "---  ------               --------------  -----         \n",
            " 0   Date                 122 non-null    object        \n",
            " 1   Day                  122 non-null    int64         \n",
            " 2   Cases_Guinea         93 non-null     float64       \n",
            " 3   Cases_Liberia        83 non-null     float64       \n",
            " 4   Cases_SierraLeone    87 non-null     float64       \n",
            " 5   Cases_Nigeria        38 non-null     float64       \n",
            " 6   Cases_Senegal        25 non-null     float64       \n",
            " 7   Cases_UnitedStates   18 non-null     float64       \n",
            " 8   Cases_Spain          16 non-null     float64       \n",
            " 9   Cases_Mali           12 non-null     float64       \n",
            " 10  Deaths_Guinea        92 non-null     float64       \n",
            " 11  Deaths_Liberia       81 non-null     float64       \n",
            " 12  Deaths_SierraLeone   87 non-null     float64       \n",
            " 13  Deaths_Nigeria       38 non-null     float64       \n",
            " 14  Deaths_Senegal       22 non-null     float64       \n",
            " 15  Deaths_UnitedStates  18 non-null     float64       \n",
            " 16  Deaths_Spain         16 non-null     float64       \n",
            " 17  Deaths_Mali          12 non-null     float64       \n",
            " 18  date_dt              122 non-null    datetime64[ns]\n",
            "dtypes: datetime64[ns](1), float64(16), int64(1), object(1)\n",
            "memory usage: 18.2+ KB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "4. format인자에 시간형식 지정자와 기호를 조합하여 전달하면 그 형식에 맞게 정리된 datetime오브젝트를 준다."
      ],
      "metadata": {
        "id": "lb7YvsinLArF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_df1= pd.DataFrame({'order_day':['01/01/15','02/01/15','03/01/15']})\n",
        "\n",
        "test_df1['date_dt1']=pd.to_datetime(test_df1['order_day'],format='%d/%m/%y')\n",
        "test_df1['date_dt2']=pd.to_datetime(test_df1['order_day'],format='%m/%d/%y')\n",
        "test_df1['date_dt3']=pd.to_datetime(test_df1['order_day'],format='%y/%m/%d')\n",
        "\n",
        "print(test_df1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rIdzjtR6LAxu",
        "outputId": "77b4619a-90d1-42a9-87b4-bd341a319a65"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  order_day   date_dt1   date_dt2   date_dt3\n",
            "0  01/01/15 2015-01-01 2015-01-01 2001-01-15\n",
            "1  02/01/15 2015-01-02 2015-02-01 2002-01-15\n",
            "2  03/01/15 2015-01-03 2015-03-01 2003-01-15\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_df2= pd.DataFrame({'order_day':['01-01-15','02-01-15','03-01-15']})\n",
        "\n",
        "test_df2['date_dt']=pd.to_datetime(test_df2['order_day'],format='%d-%m-%y')\n",
        "print(test_df2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p6HMT45RLztx",
        "outputId": "455ec346-ab5f-43f8-ba94-ae3bb65ba936"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  order_day    date_dt\n",
            "0  01-01-15 2015-01-01\n",
            "1  02-01-15 2015-01-02\n",
            "2  03-01-15 2015-01-03\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 시간 형식 지정자\n",
        "\n",
        "* %a : 요일 출력\n",
        "\n",
        "* %A : 요일 출력(긴 이름)\n",
        "\n",
        "* %w: 요일 출력(숫자, 0부터 일요일)\n",
        "\n",
        "* %d: 날짜 출력(2자리로 표시)\n",
        "\n",
        "* %b: 월 출력(영문 앞글자 3자리)\n",
        "\n",
        "* %B: 월 출력(긴 이름)\n",
        "\n",
        "* %m: 월 출력(숫자)\n",
        "\n",
        "* %y: 년 출력(2자리로 표시)\n",
        "\n",
        "* %Y: 년 출력(4자리 표시)\n",
        "* %H: 시간 출력(24시간)\n",
        "* %I: 시간 출력(12시간)\n",
        "* %p: AM or PM 출력\n",
        "* %M: 분 출력(2자리 표시)\n",
        "* %S: 초 출력(2자리로 표시)\n",
        "* %f: 마이크로초 출력\n",
        "* %z: UTC 차이 출력\n",
        "* %Z: 기준 지역 이름 출력\n",
        "* %j: 올해의 지난 일수 출력\n",
        "* %U: 올해의 지난 주 수 출력\n",
        "* %c: 날짜와 시간 출력\n",
        "* %x: 날짜 출력\n",
        "* %X: 시간 출력\n",
        "* %G:년 출력(ISO 8601 format)\n",
        "* %u: 요일 출력(ISO 8601 format)\n",
        "* %V: 올해의 지난 주수 출력(ISO 8601 format)\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "n\n",
        "### 시계열 데이터를 구분해서 추출하자\n",
        "\n",
        "now 메서드로 얻은 현재 시간의 시계열 데이터는 정밀한 단위까지 시간을 표현함.\n",
        "\n",
        "하지만 연도,월,일 뿐이라면 now메서드로 얻은 시계열 데이터를 잘라야함.\n",
        "\n"
      ],
      "metadata": {
        "id": "2QslmgL3LA4H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datetime import datetime\n",
        "\n",
        "now=datetime.now()\n",
        "print(now)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9uXEGYG9OjX9",
        "outputId": "56466d63-b352-4141-d728-7126fb891b85"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2022-10-13 23:55:44.856474\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nowDate=now.strftime('%Y-%m-%d')\n",
        "print(nowDate)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QmbSuJ4_OqEb",
        "outputId": "6ce90d01-4de3-46bd-9ab7-d58c48f9ec1a"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2022-10-13\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nowTime=now.strftime('%H:%M:%S')\n",
        "print(nowTime)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1qPXzEdAPZFz",
        "outputId": "f5819385-dcd4-4be7-b2d3-9014e6ab5353"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "23:55:44\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nowDatetime=now.strftime('%Y-%m-%d %H:%M:%S')\n",
        "print(nowDatetime)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6eYhRUUwPZoa",
        "outputId": "14d9b53e-1dc8-4df9-d95d-a29d54d7d601"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2022-10-13 23:55:44\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### datetime 오브젝트로 변환하기 -read_csv메서드\n",
        "\n",
        "앞에서는 to_datetime 메서드를 사용함\n",
        "\n",
        "하지만 datetime오브젝트로 변환하려는 열을 지정하여 데이터 집합을 불러오는 것이 더 편하다!\n",
        "\n",
        "**<datetime 오브젝트로 변환하려는 열을 지정하여 데이터 집합 불러오기>**\n",
        "\n",
        "1. read_csv메서드의 parse_dates 인자에 datetime 오브젝트로 변환하고자 하는 열의 이름을 전달하여 데이터 집합 불러오기"
      ],
      "metadata": {
        "id": "1kCoJ89MMksP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ebola1=pd.read_csv('/content/drive/MyDrive/country_timeseries.csv' , parse_dates=['Date'])\n",
        "print(ebola1.info())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LzRWg7zRLA9G",
        "outputId": "5854109a-951f-4937-c1c2-8436fceccbff"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 122 entries, 0 to 121\n",
            "Data columns (total 18 columns):\n",
            " #   Column               Non-Null Count  Dtype         \n",
            "---  ------               --------------  -----         \n",
            " 0   Date                 122 non-null    datetime64[ns]\n",
            " 1   Day                  122 non-null    int64         \n",
            " 2   Cases_Guinea         93 non-null     float64       \n",
            " 3   Cases_Liberia        83 non-null     float64       \n",
            " 4   Cases_SierraLeone    87 non-null     float64       \n",
            " 5   Cases_Nigeria        38 non-null     float64       \n",
            " 6   Cases_Senegal        25 non-null     float64       \n",
            " 7   Cases_UnitedStates   18 non-null     float64       \n",
            " 8   Cases_Spain          16 non-null     float64       \n",
            " 9   Cases_Mali           12 non-null     float64       \n",
            " 10  Deaths_Guinea        92 non-null     float64       \n",
            " 11  Deaths_Liberia       81 non-null     float64       \n",
            " 12  Deaths_SierraLeone   87 non-null     float64       \n",
            " 13  Deaths_Nigeria       38 non-null     float64       \n",
            " 14  Deaths_Senegal       22 non-null     float64       \n",
            " 15  Deaths_UnitedStates  18 non-null     float64       \n",
            " 16  Deaths_Spain         16 non-null     float64       \n",
            " 17  Deaths_Mali          12 non-null     float64       \n",
            "dtypes: datetime64[ns](1), float64(16), int64(1)\n",
            "memory usage: 17.3 KB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## datetime 오브젝트에서 날짜 정보 추출하기\n",
        "\n",
        "datetime오브젝트에는 날짜 정보를 따로 저장하고 있는 속성이 이미 준비 되어 있음.\n",
        "\n",
        "<datetime 오브젝트에서 날짜 정보 추출하기>\n",
        "1. 문자열로 지정된 날자를 시리즈에 담아 datetime오브젝트로 변환한 것"
      ],
      "metadata": {
        "id": "7pbN_QHfLBF0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "date_series=pd.Series(['2018-05-16','2018-05-17','2018-05-18'])\n",
        "d1=pd.to_datetime(date_series)\n",
        "print(d1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iD7l5o4VLBLN",
        "outputId": "827c9d66-ade3-4aec-fd57-ac174d2cc58b"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0   2018-05-16\n",
            "1   2018-05-17\n",
            "2   2018-05-18\n",
            "dtype: datetime64[ns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. datetime오브젝트의 year, month, day 속성을 이용"
      ],
      "metadata": {
        "id": "YaoJaDFNLBPC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(d1[0].year)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZYFuEX53LBUA",
        "outputId": "2b5a3aec-59bb-4ee5-9f9a-439239223246"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2018\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(d1[0].month)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kQ-PjBzXLBW0",
        "outputId": "c5b330c8-9b70-4c5c-8dae-561520a2e2dc"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(d1[0].day)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ux1AyzmhLBZP",
        "outputId": "8e4a1a18-8ba0-4b6f-a891-bb1e40cef1ac"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "16\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## dt접근자 사용하기\n",
        "\n",
        "문자열을 처리하려면 str 접근자를 사용한다.\n",
        "\n",
        "datetime오브젝트도 마찬가지로 dt접근자를 사용하면 datetime속성이나 메서드를 사용하여 처리 가능\n",
        "\n",
        "**< dt 접근자로 시계열 데이터 정리>**\n",
        "\n",
        "1.ebola 불러오고 Date열을 datetime오브젝트로 변환하여 새로운 열로 추가"
      ],
      "metadata": {
        "id": "s0tVOd5lQwFP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ebola = pd.read_csv('/content/drive/MyDrive/country_timeseries.csv' )\n",
        "ebola['date_dt']=pd.to_datetime(ebola['Date'])"
      ],
      "metadata": {
        "id": "KOMfqqOGLBbl"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. dt접근자를 사용하지 않고 인덱스가 3인 데이터의 년, 월, 일 데이터를 추출"
      ],
      "metadata": {
        "id": "I8GYvhavRT8E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(ebola[['Date','date_dt']].head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t5yjSATYRUCN",
        "outputId": "30b075ff-b34d-4b76-dc79-334bf20d2170"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "         Date    date_dt\n",
            "0    1/5/2015 2015-01-05\n",
            "1    1/4/2015 2015-01-04\n",
            "2    1/3/2015 2015-01-03\n",
            "3    1/2/2015 2015-01-02\n",
            "4  12/31/2014 2014-12-31\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(ebola['date_dt'][3].year)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3vcwqRRkRdpR",
        "outputId": "df5d6701-fd3f-4c5e-ebc3-c184957c64df"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2015\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(ebola['date_dt'][3].month)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S0t0-eqtRdwG",
        "outputId": "669963cf-6c33-4c02-f258-5ca91e4d9d87"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(ebola['date_dt'][3].day)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aV97gueqRd3O",
        "outputId": "1b0f61d7-5610-4efd-da2f-752f835bdd17"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "3. 2의 방법 : date_dt열의 특정 데이터를 인덱스로 접근해야해서 불편함\n",
        "\n",
        "다음은 dt접근자로 date_dt열에 한 번에 접근해서 year속성을 이용하여 연도 값을 추출함."
      ],
      "metadata": {
        "id": "6gxvtygYRUGm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ebola['year']=ebola['date_dt'].dt.year\n",
        "print(ebola[['Date','date_dt','year']].head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-uEAmbWvRUMP",
        "outputId": "ab44bf80-836d-4a09-aaf6-12f2c114b079"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "         Date    date_dt  year\n",
            "0    1/5/2015 2015-01-05  2015\n",
            "1    1/4/2015 2015-01-04  2015\n",
            "2    1/3/2015 2015-01-03  2015\n",
            "3    1/2/2015 2015-01-02  2015\n",
            "4  12/31/2014 2014-12-31  2014\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "4. 3과정을 응용하여 월, 일 데이터를 한 번에 추출해서 새로운 열에 추가"
      ],
      "metadata": {
        "id": "39wbFvyTRUQa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ebola['month'], ebola['day']= (ebola['date_dt'].dt.month, ebola['date_dt'].dt.day)\n",
        "print(ebola[['Date','date_dt','year','month','day']].head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YYSMfXK9RUU-",
        "outputId": "5555e642-4ad4-404e-d7c9-70e9fb1e4979"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "         Date    date_dt  year  month  day\n",
            "0    1/5/2015 2015-01-05  2015      1    5\n",
            "1    1/4/2015 2015-01-04  2015      1    4\n",
            "2    1/3/2015 2015-01-03  2015      1    3\n",
            "3    1/2/2015 2015-01-02  2015      1    2\n",
            "4  12/31/2014 2014-12-31  2014     12   31\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "5. 새로 추가한 열 자료 출력"
      ],
      "metadata": {
        "id": "ImMfebVxRUaC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(ebola.info())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SVGSM-2jRUdi",
        "outputId": "498ea33d-3eb6-42b6-b848-980e0e504ace"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 122 entries, 0 to 121\n",
            "Data columns (total 22 columns):\n",
            " #   Column               Non-Null Count  Dtype         \n",
            "---  ------               --------------  -----         \n",
            " 0   Date                 122 non-null    object        \n",
            " 1   Day                  122 non-null    int64         \n",
            " 2   Cases_Guinea         93 non-null     float64       \n",
            " 3   Cases_Liberia        83 non-null     float64       \n",
            " 4   Cases_SierraLeone    87 non-null     float64       \n",
            " 5   Cases_Nigeria        38 non-null     float64       \n",
            " 6   Cases_Senegal        25 non-null     float64       \n",
            " 7   Cases_UnitedStates   18 non-null     float64       \n",
            " 8   Cases_Spain          16 non-null     float64       \n",
            " 9   Cases_Mali           12 non-null     float64       \n",
            " 10  Deaths_Guinea        92 non-null     float64       \n",
            " 11  Deaths_Liberia       81 non-null     float64       \n",
            " 12  Deaths_SierraLeone   87 non-null     float64       \n",
            " 13  Deaths_Nigeria       38 non-null     float64       \n",
            " 14  Deaths_Senegal       22 non-null     float64       \n",
            " 15  Deaths_UnitedStates  18 non-null     float64       \n",
            " 16  Deaths_Spain         16 non-null     float64       \n",
            " 17  Deaths_Mali          12 non-null     float64       \n",
            " 18  date_dt              122 non-null    datetime64[ns]\n",
            " 19  year                 122 non-null    int64         \n",
            " 20  month                122 non-null    int64         \n",
            " 21  day                  122 non-null    int64         \n",
            "dtypes: datetime64[ns](1), float64(16), int64(4), object(1)\n",
            "memory usage: 21.1+ KB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 12-2 사례별 시계열 데이터 계산하기\n",
        "\n",
        "**<에볼라 최초 발병일 계산하기>**\n",
        "\n",
        "1. 역순으로 되어있는 데이터 프레임을 시간순으로 살펴보기"
      ],
      "metadata": {
        "id": "KSGuu44rRUgl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(ebola.iloc[-5:, :5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YrCv8c0YRUkF",
        "outputId": "502632cb-4975-4044-edde-fba3e82c781c"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "          Date  Day  Cases_Guinea  Cases_Liberia  Cases_SierraLeone\n",
            "117  3/27/2014    5         103.0            8.0                6.0\n",
            "118  3/26/2014    4          86.0            NaN                NaN\n",
            "119  3/25/2014    3          86.0            NaN                NaN\n",
            "120  3/24/2014    2          86.0            NaN                NaN\n",
            "121  3/22/2014    0          49.0            NaN                NaN\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. 121행을 보면 에볼라가 발생하기 시작한 날은 2014.03.22이다. \n",
        "\n",
        "min메서드를 이용하여 최초 발병일 찾기"
      ],
      "metadata": {
        "id": "tdPshn_CRUoL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(ebola['date_dt'].min())\n",
        "print(type(ebola['date_dt'].min()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DLEoomu7RUr8",
        "outputId": "bc6ef11a-b159-4166-97e0-433a50ded593"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2014-03-22 00:00:00\n",
            "<class 'pandas._libs.tslibs.timestamps.Timestamp'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "3. 최초 발병일을 빼고 에볼라의 진행 정도 알기"
      ],
      "metadata": {
        "id": "Y1mc9eFRT0E6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ebola['outbreak_d']=ebola['date_dt']-ebola['date_dt'].min()\n",
        "print(ebola[['Date','Day','outbreak_d']].head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JvPYorysT0KP",
        "outputId": "6725aeef-99ba-4851-ba39-5ad18eaf490f"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "         Date  Day outbreak_d\n",
            "0    1/5/2015  289   289 days\n",
            "1    1/4/2015  288   288 days\n",
            "2    1/3/2015  287   287 days\n",
            "3    1/2/2015  286   286 days\n",
            "4  12/31/2014  284   284 days\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**<파산한 은행의 개수 계산>**\n",
        "\n",
        "1. 데이터 집합 불러오기. 앞부분 Closing Date, Updated Date열의 데이터 자료형이 시계열 데이터다."
      ],
      "metadata": {
        "id": "HDPf8dPnT0Of"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "banks= pd.read_csv('/content/drive/MyDrive/banklist.csv' )\n",
        "print(banks.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FQbUW87rT0UJ",
        "outputId": "97835017-6190-46f2-912e-af431444a55f"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                           Bank Name         City  ST   CERT  \\\n",
            "0                Washington Federal Bank for Savings      Chicago  IL  30570   \n",
            "1    The Farmers and Merchants State Bank of Argonia      Argonia  KS  17719   \n",
            "2                                Fayette County Bank   Saint Elmo  IL   1802   \n",
            "3  Guaranty Bank, (d/b/a BestBank in Georgia & Mi...    Milwaukee  WI  30003   \n",
            "4                                     First NBC Bank  New Orleans  LA  58302   \n",
            "\n",
            "                 Acquiring Institution Closing Date Updated Date  \n",
            "0                   Royal Savings Bank    15-Dec-17    20-Dec-17  \n",
            "1                          Conway Bank    13-Oct-17    20-Oct-17  \n",
            "2            United Fidelity Bank, fsb    26-May-17    26-Jul-17  \n",
            "3  First-Citizens Bank & Trust Company     5-May-17    26-Jul-17  \n",
            "4                         Whitney Bank    28-Apr-17     5-Dec-17  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2.  Closing Date, Updated Date 열의 데이터 자료형은 문자열.\n",
        "\n",
        "parse_dates속성을 이용하여 datetime오브젝트로 변환하여 불러온다."
      ],
      "metadata": {
        "id": "eAj8Yg4pT0YJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "banks_no_dates = pd.read_csv('/content/drive/MyDrive/banklist.csv' )\n",
        "print(banks_no_dates.info())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZptwYRj5Uqoz",
        "outputId": "41c47623-6e20-4873-a95a-4c2552b3538d"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 555 entries, 0 to 554\n",
            "Data columns (total 7 columns):\n",
            " #   Column                 Non-Null Count  Dtype \n",
            "---  ------                 --------------  ----- \n",
            " 0   Bank Name              555 non-null    object\n",
            " 1   City                   555 non-null    object\n",
            " 2   ST                     555 non-null    object\n",
            " 3   CERT                   555 non-null    int64 \n",
            " 4   Acquiring Institution  555 non-null    object\n",
            " 5   Closing Date           555 non-null    object\n",
            " 6   Updated Date           555 non-null    object\n",
            "dtypes: int64(1), object(6)\n",
            "memory usage: 30.5+ KB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "banks = pd.read_csv('/content/drive/MyDrive/banklist.csv' , parse_dates=[5,6])\n",
        "print(banks.info())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JwH0eroKU02P",
        "outputId": "0350fcf6-ee9e-40ba-d87a-1b267f6b8061"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 555 entries, 0 to 554\n",
            "Data columns (total 7 columns):\n",
            " #   Column                 Non-Null Count  Dtype         \n",
            "---  ------                 --------------  -----         \n",
            " 0   Bank Name              555 non-null    object        \n",
            " 1   City                   555 non-null    object        \n",
            " 2   ST                     555 non-null    object        \n",
            " 3   CERT                   555 non-null    int64         \n",
            " 4   Acquiring Institution  555 non-null    object        \n",
            " 5   Closing Date           555 non-null    datetime64[ns]\n",
            " 6   Updated Date           555 non-null    datetime64[ns]\n",
            "dtypes: datetime64[ns](2), int64(1), object(4)\n",
            "memory usage: 30.5+ KB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "3. dt 접근자와 quarter속성을 이용하면 은행이 파산한 분기를 알 수 있다."
      ],
      "metadata": {
        "id": "imqRcXElT0aG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "banks['closing_quarter'], banks['closing_year'] = (banks['Closing Date'].dt.quarter, banks['Closing Date'].dt.year)\n",
        "print(banks.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wmIYetJ4LBdn",
        "outputId": "3f1e12ac-278c-4897-9da9-9c3353146043"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                           Bank Name         City  ST   CERT  \\\n",
            "0                Washington Federal Bank for Savings      Chicago  IL  30570   \n",
            "1    The Farmers and Merchants State Bank of Argonia      Argonia  KS  17719   \n",
            "2                                Fayette County Bank   Saint Elmo  IL   1802   \n",
            "3  Guaranty Bank, (d/b/a BestBank in Georgia & Mi...    Milwaukee  WI  30003   \n",
            "4                                     First NBC Bank  New Orleans  LA  58302   \n",
            "\n",
            "                 Acquiring Institution Closing Date Updated Date  \\\n",
            "0                   Royal Savings Bank   2017-12-15   2017-12-20   \n",
            "1                          Conway Bank   2017-10-13   2017-10-20   \n",
            "2            United Fidelity Bank, fsb   2017-05-26   2017-07-26   \n",
            "3  First-Citizens Bank & Trust Company   2017-05-05   2017-07-26   \n",
            "4                         Whitney Bank   2017-04-28   2017-12-05   \n",
            "\n",
            "   closing_quarter  closing_year  \n",
            "0                4          2017  \n",
            "1                4          2017  \n",
            "2                2          2017  \n",
            "3                2          2017  \n",
            "4                2          2017  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "4. 연도별로 파산한 은행이 얼마나 되는지 이용\n",
        "\n",
        "grouby 메서드를 사용하여 연도별로 파산한 은행의 개수 구하기"
      ],
      "metadata": {
        "id": "IEd5x8kDV2GU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "closing_year = banks.groupby(['closing_year']).size()\n",
        "print(closing_year)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ECNJQp1EV2Li",
        "outputId": "d9820a6c-e529-48f1-e750-857938020339"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "closing_year\n",
            "2000      2\n",
            "2001      4\n",
            "2002     11\n",
            "2003      3\n",
            "2004      4\n",
            "2007      3\n",
            "2008     25\n",
            "2009    140\n",
            "2010    157\n",
            "2011     92\n",
            "2012     51\n",
            "2013     24\n",
            "2014     18\n",
            "2015      8\n",
            "2016      5\n",
            "2017      8\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "5. 각 연도별 , 분기별로 파산한 은행의 개수 알아보기."
      ],
      "metadata": {
        "id": "QjHHz4fjV2P6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "closing_year_q = banks.groupby(['closing_year','closing_quarter']).size()\n",
        "print(closing_year_q)"
      ],
      "metadata": {
        "id": "dFuW4mpGV2UB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1dea503f-27c4-4190-de5c-896b3930f3bf"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "closing_year  closing_quarter\n",
            "2000          4                   2\n",
            "2001          1                   1\n",
            "              2                   1\n",
            "              3                   2\n",
            "2002          1                   6\n",
            "              2                   2\n",
            "              3                   1\n",
            "              4                   2\n",
            "2003          1                   1\n",
            "              2                   1\n",
            "              4                   1\n",
            "2004          1                   3\n",
            "              2                   1\n",
            "2007          1                   1\n",
            "              3                   1\n",
            "              4                   1\n",
            "2008          1                   2\n",
            "              2                   2\n",
            "              3                   9\n",
            "              4                  12\n",
            "2009          1                  21\n",
            "              2                  24\n",
            "              3                  50\n",
            "              4                  45\n",
            "2010          1                  41\n",
            "              2                  45\n",
            "              3                  41\n",
            "              4                  30\n",
            "2011          1                  26\n",
            "              2                  22\n",
            "              3                  26\n",
            "              4                  18\n",
            "2012          1                  16\n",
            "              2                  15\n",
            "              3                  12\n",
            "              4                   8\n",
            "2013          1                   4\n",
            "              2                  12\n",
            "              3                   6\n",
            "              4                   2\n",
            "2014          1                   5\n",
            "              2                   7\n",
            "              3                   2\n",
            "              4                   4\n",
            "2015          1                   4\n",
            "              2                   1\n",
            "              3                   1\n",
            "              4                   2\n",
            "2016          1                   1\n",
            "              2                   2\n",
            "              3                   2\n",
            "2017          1                   3\n",
            "              2                   3\n",
            "              4                   2\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "6. 그래프 그리기"
      ],
      "metadata": {
        "id": "CPlYVtZqV2Xu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "fig, ax= plt.subplots()\n",
        "ax=closing_year.plot()\n",
        "plt.show()\n",
        "\n",
        "fig, ax=plt.subplots\n",
        "ax=closing_year_q.plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "193fFVCDLBfy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "outputId": "ebff2cde-8775-433b-bd34-86f823fdeba7"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-52-d207a382bc0f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclosing_year_q\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: cannot unpack non-iterable function object"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "(264p~271p)\n",
        "### 시간 범위 수정하고 데이터 밀어내기 - shift메서드\n",
        "\n",
        "나라별로 에볼라 확산속도를 비교하려면 발생하기 시작한 날짜를 옮기는 것이 좋다.\n",
        "\n",
        "왜? \n",
        "\n",
        "일단 데이터프레임으로 그래프를 그려보고 에볼라의 확산속도를 비교하는 어떤 문제가 있는지 확인하자.\n",
        "\n",
        "**<에볼라 확산 속도 비교>**\n",
        "\n",
        "1. Date열을 인덱스로 지정하고 그래프 그리기"
      ],
      "metadata": {
        "id": "gXatLHc8vZyp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "ebola.index=ebola['Date']\n",
        "fig,ax=plt.subplots()\n",
        "ax=ebola.iloc[0:,1:].plot(ax=ax)\n",
        "ax.legend(fontsize=7, loc=2, borderaxespad=0.)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "YwMuCgWrvaCK",
        "outputId": "4b901a41-b258-44a7-ccb1-7a89d8d3180e"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2. 1에서 각 나라의 에볼라 발병일이 달라서 그래프 그려지는 시점이 다르다.\n",
        "\n",
        "따라서 발병일을 가장 처음 에볼라가 발병한 Guinea와 동일한 위치로 옮겨야한다."
      ],
      "metadata": {
        "id": "ROJMSPkovaKF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ebola_sub=ebola[['Day', 'Cases_Guinea','Cases_Liberia']]\n",
        "print(ebola_sub.tail(10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_rCrbx9wvaQD",
        "outputId": "75e84570-962c-4513-8946-2ad1c1702bd2"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "           Day  Cases_Guinea  Cases_Liberia\n",
            "Date                                       \n",
            "4/4/2014    13         143.0           18.0\n",
            "4/1/2014    10         127.0            8.0\n",
            "3/31/2014    9         122.0            8.0\n",
            "3/29/2014    7         112.0            7.0\n",
            "3/28/2014    6         112.0            3.0\n",
            "3/27/2014    5         103.0            8.0\n",
            "3/26/2014    4          86.0            NaN\n",
            "3/25/2014    3          86.0            NaN\n",
            "3/24/2014    2          86.0            NaN\n",
            "3/22/2014    0          49.0            NaN\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "3. 그래프 그리기 위한 데이터프레임 준비\n",
        "\n",
        "Date 열의 자료형을 datetime으로 변환하여 생성하고, 중간에 없는 날자 데이터도 포함시켜야한다."
      ],
      "metadata": {
        "id": "nbqmi3FEvaUA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ebola=pd.read_csv('/content/drive/MyDrive/country_timeseries.csv', parse_dates=['Date'])\n",
        "\n",
        "print(ebola.head().iloc[:,:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rn3okkOZvaYz",
        "outputId": "7af7733b-a256-4ca5-c420-230317124b71"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        Date  Day  Cases_Guinea  Cases_Liberia  Cases_SierraLeone\n",
            "0 2015-01-05  289        2776.0            NaN            10030.0\n",
            "1 2015-01-04  288        2775.0            NaN             9780.0\n",
            "2 2015-01-03  287        2769.0         8166.0             9722.0\n",
            "3 2015-01-02  286           NaN         8157.0                NaN\n",
            "4 2014-12-31  284        2730.0         8115.0             9633.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(ebola.tail().iloc[:,:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OokDIPunzNrn",
        "outputId": "bf5bd5e1-985c-404f-dc7a-a47f8db6b604"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "          Date  Day  Cases_Guinea  Cases_Liberia  Cases_SierraLeone\n",
            "117 2014-03-27    5         103.0            8.0                6.0\n",
            "118 2014-03-26    4          86.0            NaN                NaN\n",
            "119 2014-03-25    3          86.0            NaN                NaN\n",
            "120 2014-03-24    2          86.0            NaN                NaN\n",
            "121 2014-03-22    0          49.0            NaN                NaN\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "4.Date열을 인덱스로 지정한 다음 ebola데이터 프레임의 date열의 최댓값과 최솟값으로 시간 범위 생성.\n",
        "\n",
        "->날짜가 아예 없었던 데이터의 인덱스를 생성할 수 있다."
      ],
      "metadata": {
        "id": "0uZNhjykvacY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ebola.index=ebola['Date']\n",
        "new_idx=pd.date_range(ebola.index.min(), ebola.index.max())"
      ],
      "metadata": {
        "id": "3C3IlBTfvafz"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "5. 시간 순서를 맞춰준다."
      ],
      "metadata": {
        "id": "aG4nyYXrvai5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(new_idx)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "usaksi7AvamK",
        "outputId": "5045f02b-8285-4140-e9b9-96977c2e8d65"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DatetimeIndex(['2014-03-22', '2014-03-23', '2014-03-24', '2014-03-25',\n",
            "               '2014-03-26', '2014-03-27', '2014-03-28', '2014-03-29',\n",
            "               '2014-03-30', '2014-03-31',\n",
            "               ...\n",
            "               '2014-12-27', '2014-12-28', '2014-12-29', '2014-12-30',\n",
            "               '2014-12-31', '2015-01-01', '2015-01-02', '2015-01-03',\n",
            "               '2015-01-04', '2015-01-05'],\n",
            "              dtype='datetime64[ns]', length=290, freq='D')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_idx=reversed(new_idx)"
      ],
      "metadata": {
        "id": "tNbqBqhkzpjw"
      },
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "6. reindex메서드를 사용하여 새로운 인덱스로 지정하기."
      ],
      "metadata": {
        "id": "JG09fpjZvapb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ebola=ebola.reindex(new_idx)\n",
        "print(ebola.head().iloc[:,:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "50NdYA8UvatG",
        "outputId": "5a2dfaad-18f9-4146-85c4-2ecd006a2075"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                 Date    Day  Cases_Guinea  Cases_Liberia  Cases_SierraLeone\n",
            "Date                                                                        \n",
            "2015-01-05 2015-01-05  289.0        2776.0            NaN            10030.0\n",
            "2015-01-04 2015-01-04  288.0        2775.0            NaN             9780.0\n",
            "2015-01-03 2015-01-03  287.0        2769.0         8166.0             9722.0\n",
            "2015-01-02 2015-01-02  286.0           NaN         8157.0                NaN\n",
            "2015-01-01        NaT    NaN           NaN            NaN                NaN\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(ebola.tail().iloc[:,:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PoBZjO-Z0CFj",
        "outputId": "d5cbc5c4-c189-458a-e9db-35650350f700"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                 Date  Day  Cases_Guinea  Cases_Liberia  Cases_SierraLeone\n",
            "Date                                                                      \n",
            "2014-03-26 2014-03-26  4.0          86.0            NaN                NaN\n",
            "2014-03-25 2014-03-25  3.0          86.0            NaN                NaN\n",
            "2014-03-24 2014-03-24  2.0          86.0            NaN                NaN\n",
            "2014-03-23        NaT  NaN           NaN            NaN                NaN\n",
            "2014-03-22 2014-03-22  0.0          49.0            NaN                NaN\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "7.각 나라의 에볼라 발병일 옮기기\n",
        "\n",
        "last_valid_index, first_valid_index 메서드를 사용하여 각 나라의 에볼라 발병일을 구한다.\n",
        "\n",
        "- 각 메서드는 첫번째와 마지막 인덱스를 반환한다."
      ],
      "metadata": {
        "id": "gBsLjndXvawM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "last_valid = ebola.apply(pd.Series.last_valid_index)\n",
        "print(last_valid)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QpM5DAaU0RkF",
        "outputId": "abc2fde1-6489-47ba-ca40-e88d156b6d4f"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Date                  2014-03-22\n",
            "Day                   2014-03-22\n",
            "Cases_Guinea          2014-03-22\n",
            "Cases_Liberia         2014-03-27\n",
            "Cases_SierraLeone     2014-03-27\n",
            "Cases_Nigeria         2014-07-23\n",
            "Cases_Senegal         2014-08-31\n",
            "Cases_UnitedStates    2014-10-01\n",
            "Cases_Spain           2014-10-08\n",
            "Cases_Mali            2014-10-22\n",
            "Deaths_Guinea         2014-03-22\n",
            "Deaths_Liberia        2014-03-27\n",
            "Deaths_SierraLeone    2014-03-27\n",
            "Deaths_Nigeria        2014-07-23\n",
            "Deaths_Senegal        2014-09-07\n",
            "Deaths_UnitedStates   2014-10-01\n",
            "Deaths_Spain          2014-10-08\n",
            "Deaths_Mali           2014-10-22\n",
            "dtype: datetime64[ns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "first_valid = ebola.apply(pd.Series.first_valid_index)\n",
        "print(first_valid)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vXYclzC70RnP",
        "outputId": "d9eed463-81f3-4091-8ddf-db8f2ddcf80e"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Date                  2015-01-05\n",
            "Day                   2015-01-05\n",
            "Cases_Guinea          2015-01-05\n",
            "Cases_Liberia         2015-01-03\n",
            "Cases_SierraLeone     2015-01-05\n",
            "Cases_Nigeria         2014-12-07\n",
            "Cases_Senegal         2014-12-07\n",
            "Cases_UnitedStates    2014-12-07\n",
            "Cases_Spain           2014-12-07\n",
            "Cases_Mali            2014-12-07\n",
            "Deaths_Guinea         2015-01-05\n",
            "Deaths_Liberia        2015-01-03\n",
            "Deaths_SierraLeone    2015-01-05\n",
            "Deaths_Nigeria        2014-12-07\n",
            "Deaths_Senegal        2014-12-07\n",
            "Deaths_UnitedStates   2014-12-07\n",
            "Deaths_Spain          2014-12-07\n",
            "Deaths_Mali           2014-12-07\n",
            "dtype: datetime64[ns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "8.각 나라의 에볼라 발병일을 동일한 출발선으로 옮기기\n",
        " -> 에볼라가 가장 처음 발병한 날에서 각 나라의 에볼라 발병일 뺀 만큼 옮긴다."
      ],
      "metadata": {
        "id": "uZPP1R-Q0gGx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "earliest_date = ebola.index.min()\n",
        "print(earliest_date)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rkZahUae0Rp0",
        "outputId": "a6b222f0-06a7-470f-8c74-e8db9082c028"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2014-03-22 00:00:00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shift_values= last_valid - earliest_date\n",
        "print(shift_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YiqSkbXD0sEs",
        "outputId": "5dc3c7b4-8fd1-4107-8848-1ef010fcfeb4"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Date                    0 days\n",
            "Day                     0 days\n",
            "Cases_Guinea            0 days\n",
            "Cases_Liberia           5 days\n",
            "Cases_SierraLeone       5 days\n",
            "Cases_Nigeria         123 days\n",
            "Cases_Senegal         162 days\n",
            "Cases_UnitedStates    193 days\n",
            "Cases_Spain           200 days\n",
            "Cases_Mali            214 days\n",
            "Deaths_Guinea           0 days\n",
            "Deaths_Liberia          5 days\n",
            "Deaths_SierraLeone      5 days\n",
            "Deaths_Nigeria        123 days\n",
            "Deaths_Senegal        169 days\n",
            "Deaths_UnitedStates   193 days\n",
            "Deaths_Spain          200 days\n",
            "Deaths_Mali           214 days\n",
            "dtype: timedelta64[ns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "9. 각 나라의 에볼라 발병일 옮기기\n",
        "* shift 메서드를 사용하여 모든 열의 값을 shift_values 값만큼 옮긴다.\n",
        "* shift 메서드: 인잣값만큼 데이터를 밀어낸다."
      ],
      "metadata": {
        "id": "DvZKRltZ0fjJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ebola_dict={}\n",
        "for idx, col in enumerate(ebola):\n",
        "  d=shift_values[idx].days\n",
        "  shifted=ebola[col].shift(d)\n",
        "  ebola_dict[col]=shifted"
      ],
      "metadata": {
        "id": "vU1iJSyf0RsR"
      },
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "10. ebola_dict에는 시간을 다시 설정한 데이터가 딕셔너리 형태로 저장 되어있다. 이제는 DateFrame으로 변환하자."
      ],
      "metadata": {
        "id": "yBobaANovazj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ebola_shift = pd.DataFrame(ebola_dict)"
      ],
      "metadata": {
        "id": "Ir-JYJwN1UGB"
      },
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "11. 확인"
      ],
      "metadata": {
        "id": "J8aS2z2Fva3W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(ebola_shift.tail())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y706ym-0va6n",
        "outputId": "1bebc8a5-66a6-41d6-b3ea-98595446ec5f"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                 Date  Day  Cases_Guinea  Cases_Liberia  Cases_SierraLeone  \\\n",
            "Date                                                                         \n",
            "2014-03-26 2014-03-26  4.0          86.0            8.0                2.0   \n",
            "2014-03-25 2014-03-25  3.0          86.0            NaN                NaN   \n",
            "2014-03-24 2014-03-24  2.0          86.0            7.0                NaN   \n",
            "2014-03-23        NaT  NaN           NaN            3.0                2.0   \n",
            "2014-03-22 2014-03-22  0.0          49.0            8.0                6.0   \n",
            "\n",
            "            Cases_Nigeria  Cases_Senegal  Cases_UnitedStates  Cases_Spain  \\\n",
            "Date                                                                        \n",
            "2014-03-26            1.0            NaN                 1.0          1.0   \n",
            "2014-03-25            NaN            NaN                 NaN          NaN   \n",
            "2014-03-24            NaN            NaN                 NaN          NaN   \n",
            "2014-03-23            NaN            NaN                 NaN          NaN   \n",
            "2014-03-22            0.0            1.0                 1.0          1.0   \n",
            "\n",
            "            Cases_Mali  Deaths_Guinea  Deaths_Liberia  Deaths_SierraLeone  \\\n",
            "Date                                                                        \n",
            "2014-03-26         NaN           62.0             4.0                 2.0   \n",
            "2014-03-25         NaN           60.0             NaN                 NaN   \n",
            "2014-03-24         NaN           59.0             2.0                 NaN   \n",
            "2014-03-23         NaN            NaN             3.0                 2.0   \n",
            "2014-03-22         1.0           29.0             6.0                 5.0   \n",
            "\n",
            "            Deaths_Nigeria  Deaths_Senegal  Deaths_UnitedStates  Deaths_Spain  \\\n",
            "Date                                                                            \n",
            "2014-03-26             1.0             NaN                  0.0           1.0   \n",
            "2014-03-25             NaN             NaN                  NaN           NaN   \n",
            "2014-03-24             NaN             NaN                  NaN           NaN   \n",
            "2014-03-23             NaN             NaN                  NaN           NaN   \n",
            "2014-03-22             0.0             0.0                  0.0           1.0   \n",
            "\n",
            "            Deaths_Mali  \n",
            "Date                     \n",
            "2014-03-26          NaN  \n",
            "2014-03-25          NaN  \n",
            "2014-03-24          NaN  \n",
            "2014-03-23          NaN  \n",
            "2014-03-22          1.0  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "12. 인덱스를 Day열로 지정하고 필요없는 나머지 삭제하기"
      ],
      "metadata": {
        "id": "Zs9i_EGBva9U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ebola_shift.index=ebola_shift['Day']\n",
        "ebola_shift=ebola_shift.drop(['Date','Day'],axis=1)\n",
        "\n",
        "print(ebola_shift.tail())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZsOIVcFivbAo",
        "outputId": "f467f527-ddcb-474f-e1cb-1b5552017bf1"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     Cases_Guinea  Cases_Liberia  Cases_SierraLeone  Cases_Nigeria  \\\n",
            "Day                                                                  \n",
            "4.0          86.0            8.0                2.0            1.0   \n",
            "3.0          86.0            NaN                NaN            NaN   \n",
            "2.0          86.0            7.0                NaN            NaN   \n",
            "NaN           NaN            3.0                2.0            NaN   \n",
            "0.0          49.0            8.0                6.0            0.0   \n",
            "\n",
            "     Cases_Senegal  Cases_UnitedStates  Cases_Spain  Cases_Mali  \\\n",
            "Day                                                               \n",
            "4.0            NaN                 1.0          1.0         NaN   \n",
            "3.0            NaN                 NaN          NaN         NaN   \n",
            "2.0            NaN                 NaN          NaN         NaN   \n",
            "NaN            NaN                 NaN          NaN         NaN   \n",
            "0.0            1.0                 1.0          1.0         1.0   \n",
            "\n",
            "     Deaths_Guinea  Deaths_Liberia  Deaths_SierraLeone  Deaths_Nigeria  \\\n",
            "Day                                                                      \n",
            "4.0           62.0             4.0                 2.0             1.0   \n",
            "3.0           60.0             NaN                 NaN             NaN   \n",
            "2.0           59.0             2.0                 NaN             NaN   \n",
            "NaN            NaN             3.0                 2.0             NaN   \n",
            "0.0           29.0             6.0                 5.0             0.0   \n",
            "\n",
            "     Deaths_Senegal  Deaths_UnitedStates  Deaths_Spain  Deaths_Mali  \n",
            "Day                                                                  \n",
            "4.0             NaN                  0.0           1.0          NaN  \n",
            "3.0             NaN                  NaN           NaN          NaN  \n",
            "2.0             NaN                  NaN           NaN          NaN  \n",
            "NaN             NaN                  NaN           NaN          NaN  \n",
            "0.0             0.0                  0.0           1.0          1.0  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig,ax=plt.subplots()\n",
        "ax=ebola_shift.iloc[:,:].plot(ax=ax)\n",
        "ax.legend(fontsize=7, loc=2, borderaxespad=0.)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "WDvHSN-b2L13",
        "outputId": "246f7be6-d986-45c5-9ae8-324e3f0b5238"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZFda4IZ3LBin"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}