분석 소스 코드.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r4IKwa_blla0",
        "colab_type": "text"
      },
      "source": [
        "# 코로나\n",
        "* https://www.egbiz.or.kr/prjCategory/a/m/selectPrjView.do?prjDegreeId=PD000000019032\n",
        "* https://zzsza.github.io/data/2019/02/06/prophet/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5axf3XS2lla1",
        "colab_type": "text"
      },
      "source": [
        "* 지역별로 나눠서, 이상치 제거해서 시계열 돌려보기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-NXxMWBylla1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from fbprophet import Prophet"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "Y2qemhHKlla4",
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          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "outputId": "d88c6bc5-73b2-4a9d-b7a0-a8a8dd93dc32"
      },
      "source": [
        "data = pd.read_csv('/content/1-1_전체_데이터_data.csv')\n",
        "data.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4194 entries, 0 to 4193\n",
            "Data columns (total 9 columns):\n",
            " #   Column            Non-Null Count  Dtype \n",
            "---  ------            --------------  ----- \n",
            " 0   기준일(발병일, 확진일 선택)  4194 non-null   object\n",
            " 1   지역 + 시            4194 non-null   object\n",
            " 2   무증상/조사중           4194 non-null   object\n",
            " 3   필터(무증상,조사중 제외)    4194 non-null   object\n",
            " 4   무증상/조사중 기준일       4194 non-null   object\n",
            " 5   지역                4194 non-null   object\n",
            " 6   지역 (group)        4194 non-null   object\n",
            " 7   확진일자              4194 non-null   object\n",
            " 8   레코드 수             4194 non-null   int64 \n",
            "dtypes: int64(1), object(8)\n",
            "memory usage: 295.0+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pmBydADzgPWm",
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        "colab": {
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          "height": 204
        },
        "outputId": "fccc1a07-b733-4e8e-84bf-190f6be78710"
      },
      "source": [
        "data.head() # 원 데이터"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>기준일(발병일, 확진일 선택)</th>\n",
              "      <th>지역 + 시</th>\n",
              "      <th>무증상/조사중</th>\n",
              "      <th>필터(무증상,조사중 제외)</th>\n",
              "      <th>무증상/조사중 기준일</th>\n",
              "      <th>지역</th>\n",
              "      <th>지역 (group)</th>\n",
              "      <th>확진일자</th>\n",
              "      <th>레코드 수</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020. 1. 26.</td>\n",
              "      <td>고양시</td>\n",
              "      <td></td>\n",
              "      <td>참</td>\n",
              "      <td>2020. 1. 26.</td>\n",
              "      <td>고양일산서</td>\n",
              "      <td>고양</td>\n",
              "      <td>2020-01-26</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020. 1. 27.</td>\n",
              "      <td>평택시</td>\n",
              "      <td></td>\n",
              "      <td>참</td>\n",
              "      <td>2020. 1. 27.</td>\n",
              "      <td>평택</td>\n",
              "      <td>평택</td>\n",
              "      <td>2020-01-27</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020. 2. 1.</td>\n",
              "      <td>부천시</td>\n",
              "      <td></td>\n",
              "      <td>참</td>\n",
              "      <td>2020. 2. 1.</td>\n",
              "      <td>부천</td>\n",
              "      <td>부천</td>\n",
              "      <td>2020-02-01</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020. 2. 2.</td>\n",
              "      <td>부천시</td>\n",
              "      <td></td>\n",
              "      <td>참</td>\n",
              "      <td>2020. 2. 2.</td>\n",
              "      <td>부천</td>\n",
              "      <td>부천</td>\n",
              "      <td>2020-02-02</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020. 2. 2.</td>\n",
              "      <td>수원시</td>\n",
              "      <td></td>\n",
              "      <td>참</td>\n",
              "      <td>2020. 2. 2.</td>\n",
              "      <td>수원장안</td>\n",
              "      <td>수원</td>\n",
              "      <td>2020-02-02</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  기준일(발병일, 확진일 선택) 지역 + 시  무증상/조사중  ... 지역 (group)       확진일자 레코드 수\n",
              "0     2020. 1. 26.     고양시          ...         고양 2020-01-26     1\n",
              "1     2020. 1. 27.     평택시          ...         평택 2020-01-27     1\n",
              "2      2020. 2. 1.     부천시          ...         부천 2020-02-01     1\n",
              "3      2020. 2. 2.     부천시          ...         부천 2020-02-02     1\n",
              "4      2020. 2. 2.     수원시          ...         수원 2020-02-02     1\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
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        "scrolled": true,
        "id": "URzH8cLtlla7",
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          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "95f35bbc-823f-4593-a7ff-5ff5f66f6867"
      },
      "source": [
        "data.iloc[:,7] = pd.to_datetime(data.iloc[:,7])\n",
        "a = data.iloc[:,[1,7]]\n",
        "a.columns = ['지역','확진일']\n",
        "a.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>지역</th>\n",
              "      <th>확진일</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>고양시</td>\n",
              "      <td>2020-01-26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>평택시</td>\n",
              "      <td>2020-01-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>부천시</td>\n",
              "      <td>2020-02-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>부천시</td>\n",
              "      <td>2020-02-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>수원시</td>\n",
              "      <td>2020-02-02</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    지역        확진일\n",
              "0  고양시 2020-01-26\n",
              "1  평택시 2020-01-27\n",
              "2  부천시 2020-02-01\n",
              "3  부천시 2020-02-02\n",
              "4  수원시 2020-02-02"
            ]
          },
          "metadata": {
            "tags": []
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        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_4euqOADlla-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "outputId": "697edabb-7777-4966-8ce6-658828d7153a"
      },
      "source": [
        "b = a['지역'].unique()\n",
        "b"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['고양시', '평택시', '부천시', '수원시', '구리시', '시흥시', '김포시', '이천시', '포천시',\n",
              "       '안양시', '용인시', '파주시', '남양주시', '성남시', '과천시', '광명시', '화성시', '오산시',\n",
              "       '군포시', '의왕시', '안산시', '광주시', '안성시', '의정부시', '하남시', '동두천시', '양주시',\n",
              "       '가평군', '양평군', '여주시', '연천군'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
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      "cell_type": "code",
      "metadata": {
        "id": "z5q8qJSG0Jpx",
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          "height": 419
        },
        "outputId": "47e39c70-5b2b-4550-9a5d-f86be2fb633d"
      },
      "source": [
        "c = pd.DataFrame(pd.date_range('2020-01-26', periods=240))\n",
        "c.columns = ['ds']\n",
        "c['y'] = 0\n",
        "c"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th></th>\n",
              "      <th>ds</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-01-26</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-01-27</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-01-28</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-01-29</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-01-30</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>235</th>\n",
              "      <td>2020-09-17</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>236</th>\n",
              "      <td>2020-09-18</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>237</th>\n",
              "      <td>2020-09-19</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>238</th>\n",
              "      <td>2020-09-20</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>239</th>\n",
              "      <td>2020-09-21</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>240 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            ds  y\n",
              "0   2020-01-26  0\n",
              "1   2020-01-27  0\n",
              "2   2020-01-28  0\n",
              "3   2020-01-29  0\n",
              "4   2020-01-30  0\n",
              "..         ... ..\n",
              "235 2020-09-17  0\n",
              "236 2020-09-18  0\n",
              "237 2020-09-19  0\n",
              "238 2020-09-20  0\n",
              "239 2020-09-21  0\n",
              "\n",
              "[240 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Rk1azYPllbA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for i in range(len(b)):\n",
        "    globals()['a_%s' % b[i]] = pd.DataFrame(pd.date_range('2020-01-26', periods=240))\n",
        "    globals()['a_%s' % b[i]] = pd.DataFrame(a[a['지역'] == b[i]]['확진일'].value_counts()).sort_index()\n",
        "    globals()['a_%s' % b[i]].reset_index(inplace = True)\n",
        "    globals()['a_%s' % b[i]].columns = ['ds','y']\n",
        "    globals()['b_%s' % b[i]] = pd.merge(c, globals()['a_%s' % b[i]], on='ds', how = 'left')\n",
        "    globals()['b_%s' % b[i]]['y'] = np.where(pd.notnull(globals()['b_%s' % b[i]]['y_y']) == True, globals()['b_%s' % b[i]]['y_y'], globals()['b_%s' % b[i]]['y_x'])\n",
        "    del globals()['b_%s' % b[i]]['y_x'], globals()['b_%s' % b[i]]['y_y']\n",
        "    globals()['b_%s' % b[i]]['floor'] = 0\n",
        "    globals()['b_%s' % b[i]]['cap'] = 100"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jbxuIj5_f7X-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "12f89090-cb00-4086-8e07-dfda7122f6f0"
      },
      "source": [
        "b_고양시 # 시게열 예측에 활용하기 위한 데이터 프레임"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>ds</th>\n",
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              "      <th>floor</th>\n",
              "      <th>cap</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-01-26</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-01-27</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-01-28</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-01-29</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-01-30</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>235</th>\n",
              "      <td>2020-09-17</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>236</th>\n",
              "      <td>2020-09-18</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>237</th>\n",
              "      <td>2020-09-19</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>238</th>\n",
              "      <td>2020-09-20</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>239</th>\n",
              "      <td>2020-09-21</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>240 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            ds     y  floor  cap\n",
              "0   2020-01-26   1.0      0  100\n",
              "1   2020-01-27   0.0      0  100\n",
              "2   2020-01-28   0.0      0  100\n",
              "3   2020-01-29   0.0      0  100\n",
              "4   2020-01-30   0.0      0  100\n",
              "..         ...   ...    ...  ...\n",
              "235 2020-09-17   4.0      0  100\n",
              "236 2020-09-18  12.0      0  100\n",
              "237 2020-09-19   5.0      0  100\n",
              "238 2020-09-20   2.0      0  100\n",
              "239 2020-09-21   6.0      0  100\n",
              "\n",
              "[240 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UhShWgRKllbC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5d156792-f647-4792-b846-408b8b55f7ea"
      },
      "source": [
        "# 시계열 모델 구현\n",
        "for i in range(len(b)): \n",
        "    globals()['m1_%s' % b[i]] = Prophet(growth='logistic', changepoints=['2020-03-28', '2020-05-13', '2020-07-01', '2020-08-23', '2020-09-06', '2020-09-20'])\n",
        "    globals()['m1_%s' % b[i]].fit(globals()['b_%s' % b[i]])\n",
        "    globals()['future1_%s' % b[i]] = globals()['m1_%s' % b[i]].make_future_dataframe(periods = 30)\n",
        "    globals()['future1_%s' % b[i]]['floor'] = 0\n",
        "    globals()['future1_%s' % b[i]]['cap'] = 100\n",
        "    globals()['forecast1_%s' % b[i]] = globals()['m1_%s' % b[i]].predict(globals()['future1_%s' % b[i]])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o7VFAPdnllbE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "2c4e9358-b4b9-44bf-a5eb-24a8088edcec"
      },
      "source": [
        "# 시계열 예측 그래프\n",
        "fig1 = m1_고양시.plot(forecast1_고양시)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5uHiyK3haokd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "6aacdf57-0d62-4fa9-a12b-56ecb99b2f7f"
      },
      "source": [
        "forecast1_고양시.iloc[248:253,[0,17]] # 시계열 예측 데이터 프레임"
      ],
      "execution_count": null,
      "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>ds</th>\n",
              "      <th>yhat</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>248</th>\n",
              "      <td>2020-09-30</td>\n",
              "      <td>9.527617</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249</th>\n",
              "      <td>2020-10-01</td>\n",
              "      <td>9.551300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>250</th>\n",
              "      <td>2020-10-02</td>\n",
              "      <td>9.459476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>251</th>\n",
              "      <td>2020-10-03</td>\n",
              "      <td>9.782426</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>252</th>\n",
              "      <td>2020-10-04</td>\n",
              "      <td>9.555510</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            ds      yhat\n",
              "248 2020-09-30  9.527617\n",
              "249 2020-10-01  9.551300\n",
              "250 2020-10-02  9.459476\n",
              "251 2020-10-03  9.782426\n",
              "252 2020-10-04  9.555510"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-NB5gqfqiDMb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "outputId": "65d3ee77-8a74-4c2d-8d6c-6abf23faf2ca"
      },
      "source": [
        "forecast1_고양시.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 270 entries, 0 to 269\n",
            "Data columns (total 18 columns):\n",
            " #   Column                      Non-Null Count  Dtype         \n",
            "---  ------                      --------------  -----         \n",
            " 0   ds                          270 non-null    datetime64[ns]\n",
            " 1   trend                       270 non-null    float64       \n",
            " 2   cap                         270 non-null    int64         \n",
            " 3   floor                       270 non-null    int64         \n",
            " 4   yhat_lower                  270 non-null    float64       \n",
            " 5   yhat_upper                  270 non-null    float64       \n",
            " 6   trend_lower                 270 non-null    float64       \n",
            " 7   trend_upper                 270 non-null    float64       \n",
            " 8   additive_terms              270 non-null    float64       \n",
            " 9   additive_terms_lower        270 non-null    float64       \n",
            " 10  additive_terms_upper        270 non-null    float64       \n",
            " 11  weekly                      270 non-null    float64       \n",
            " 12  weekly_lower                270 non-null    float64       \n",
            " 13  weekly_upper                270 non-null    float64       \n",
            " 14  multiplicative_terms        270 non-null    float64       \n",
            " 15  multiplicative_terms_lower  270 non-null    float64       \n",
            " 16  multiplicative_terms_upper  270 non-null    float64       \n",
            " 17  yhat                        270 non-null    float64       \n",
            "dtypes: datetime64[ns](1), float64(15), int64(2)\n",
            "memory usage: 38.1 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "upyiPuKH9JdO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "326728f8-f91f-4ce1-ed76-e64e8e916463"
      },
      "source": [
        "d = pd.DataFrame(b)\n",
        "d.columns = ['지역']\n",
        "e = []\n",
        "f = []\n",
        "g = []\n",
        "h = []\n",
        "j = []\n",
        "for i in range(len(b)):\n",
        "  e.append(globals()['forecast1_%s' % b[i]].iloc[248,17])\n",
        "  f.append(globals()['forecast1_%s' % b[i]].iloc[249,17])\n",
        "  g.append(globals()['forecast1_%s' % b[i]].iloc[250,17])\n",
        "  h.append(globals()['forecast1_%s' % b[i]].iloc[251,17])\n",
        "  j.append(globals()['forecast1_%s' % b[i]].iloc[252,17])\n",
        "d['9월 30일 예측'] = e\n",
        "d['10월 1일 예측'] = f\n",
        "d['10월 2일 예측'] = g\n",
        "d['10월 3일 예측'] = h\n",
        "d['10월 4일 예측'] = j\n",
        "d"
      ],
      "execution_count": null,
      "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>지역</th>\n",
              "      <th>9월 30일 예측</th>\n",
              "      <th>10월 1일 예측</th>\n",
              "      <th>10월 2일 예측</th>\n",
              "      <th>10월 3일 예측</th>\n",
              "      <th>10월 4일 예측</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>고양시</td>\n",
              "      <td>9.527617</td>\n",
              "      <td>9.551300</td>\n",
              "      <td>9.459476</td>\n",
              "      <td>9.782426</td>\n",
              "      <td>9.555510</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>평택시</td>\n",
              "      <td>1.162909</td>\n",
              "      <td>1.339653</td>\n",
              "      <td>1.958205</td>\n",
              "      <td>1.810958</td>\n",
              "      <td>0.901536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>부천시</td>\n",
              "      <td>3.129594</td>\n",
              "      <td>2.754975</td>\n",
              "      <td>2.555758</td>\n",
              "      <td>2.531941</td>\n",
              "      <td>2.233509</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>수원시</td>\n",
              "      <td>1.323410</td>\n",
              "      <td>1.156042</td>\n",
              "      <td>1.044469</td>\n",
              "      <td>1.156081</td>\n",
              "      <td>0.658482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>구리시</td>\n",
              "      <td>0.008220</td>\n",
              "      <td>-0.134213</td>\n",
              "      <td>-0.020301</td>\n",
              "      <td>-0.077285</td>\n",
              "      <td>0.185843</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>시흥시</td>\n",
              "      <td>1.012648</td>\n",
              "      <td>1.185181</td>\n",
              "      <td>0.986503</td>\n",
              "      <td>0.987714</td>\n",
              "      <td>0.881433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>김포시</td>\n",
              "      <td>0.142650</td>\n",
              "      <td>-0.001262</td>\n",
              "      <td>0.027520</td>\n",
              "      <td>0.171431</td>\n",
              "      <td>-0.167282</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>이천시</td>\n",
              "      <td>-0.097564</td>\n",
              "      <td>0.312942</td>\n",
              "      <td>0.137012</td>\n",
              "      <td>-0.009596</td>\n",
              "      <td>-0.151508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>포천시</td>\n",
              "      <td>0.317668</td>\n",
              "      <td>-0.087185</td>\n",
              "      <td>0.086326</td>\n",
              "      <td>-0.231773</td>\n",
              "      <td>0.056697</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>안양시</td>\n",
              "      <td>0.006727</td>\n",
              "      <td>0.087445</td>\n",
              "      <td>0.141230</td>\n",
              "      <td>-0.101258</td>\n",
              "      <td>0.127682</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>용인시</td>\n",
              "      <td>1.487456</td>\n",
              "      <td>1.662997</td>\n",
              "      <td>2.072611</td>\n",
              "      <td>1.721490</td>\n",
              "      <td>1.302787</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>파주시</td>\n",
              "      <td>-0.019550</td>\n",
              "      <td>-0.220063</td>\n",
              "      <td>-0.105490</td>\n",
              "      <td>0.180947</td>\n",
              "      <td>0.232517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>남양주시</td>\n",
              "      <td>-0.210516</td>\n",
              "      <td>-0.062602</td>\n",
              "      <td>0.440353</td>\n",
              "      <td>-0.033058</td>\n",
              "      <td>-0.324275</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>성남시</td>\n",
              "      <td>1.780776</td>\n",
              "      <td>1.952422</td>\n",
              "      <td>1.780830</td>\n",
              "      <td>1.666443</td>\n",
              "      <td>1.343077</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>과천시</td>\n",
              "      <td>0.059407</td>\n",
              "      <td>-0.030175</td>\n",
              "      <td>-0.060035</td>\n",
              "      <td>0.029546</td>\n",
              "      <td>-0.087707</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>광명시</td>\n",
              "      <td>0.022698</td>\n",
              "      <td>-0.093317</td>\n",
              "      <td>-0.180339</td>\n",
              "      <td>0.109554</td>\n",
              "      <td>0.015990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>화성시</td>\n",
              "      <td>0.015803</td>\n",
              "      <td>0.075187</td>\n",
              "      <td>0.193964</td>\n",
              "      <td>0.164258</td>\n",
              "      <td>-0.216850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>오산시</td>\n",
              "      <td>-0.071750</td>\n",
              "      <td>0.074704</td>\n",
              "      <td>0.016123</td>\n",
              "      <td>0.016122</td>\n",
              "      <td>-0.069597</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>군포시</td>\n",
              "      <td>0.064099</td>\n",
              "      <td>-0.133404</td>\n",
              "      <td>0.289743</td>\n",
              "      <td>0.035817</td>\n",
              "      <td>-0.106556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>의왕시</td>\n",
              "      <td>-0.059559</td>\n",
              "      <td>-0.059559</td>\n",
              "      <td>0.029048</td>\n",
              "      <td>0.176725</td>\n",
              "      <td>-0.029153</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>안산시</td>\n",
              "      <td>0.005594</td>\n",
              "      <td>0.529501</td>\n",
              "      <td>-0.110788</td>\n",
              "      <td>-0.023455</td>\n",
              "      <td>-0.189544</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>광주시</td>\n",
              "      <td>0.099787</td>\n",
              "      <td>-0.054470</td>\n",
              "      <td>0.192348</td>\n",
              "      <td>0.068943</td>\n",
              "      <td>-0.141984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>안성시</td>\n",
              "      <td>0.062385</td>\n",
              "      <td>-0.025252</td>\n",
              "      <td>-0.083677</td>\n",
              "      <td>0.033172</td>\n",
              "      <td>0.032116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>의정부시</td>\n",
              "      <td>0.253217</td>\n",
              "      <td>0.196685</td>\n",
              "      <td>-0.057609</td>\n",
              "      <td>-0.001134</td>\n",
              "      <td>-0.337473</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>하남시</td>\n",
              "      <td>-0.106899</td>\n",
              "      <td>0.153474</td>\n",
              "      <td>0.124528</td>\n",
              "      <td>-0.078011</td>\n",
              "      <td>0.064270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>동두천시</td>\n",
              "      <td>0.050356</td>\n",
              "      <td>-0.037294</td>\n",
              "      <td>-0.037294</td>\n",
              "      <td>0.021139</td>\n",
              "      <td>-0.036354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>양주시</td>\n",
              "      <td>-0.073863</td>\n",
              "      <td>0.097537</td>\n",
              "      <td>0.011837</td>\n",
              "      <td>-0.016730</td>\n",
              "      <td>-0.099154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>가평군</td>\n",
              "      <td>0.016887</td>\n",
              "      <td>-0.101557</td>\n",
              "      <td>-0.131165</td>\n",
              "      <td>-0.012715</td>\n",
              "      <td>0.073991</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>양평군</td>\n",
              "      <td>-0.224706</td>\n",
              "      <td>0.037905</td>\n",
              "      <td>0.738246</td>\n",
              "      <td>-0.108061</td>\n",
              "      <td>0.004587</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>여주시</td>\n",
              "      <td>0.024857</td>\n",
              "      <td>-0.004061</td>\n",
              "      <td>-0.032979</td>\n",
              "      <td>0.053764</td>\n",
              "      <td>-0.061963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>연천군</td>\n",
              "      <td>-0.037733</td>\n",
              "      <td>0.020769</td>\n",
              "      <td>0.254792</td>\n",
              "      <td>-0.067001</td>\n",
              "      <td>-0.066547</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      지역  9월 30일 예측  10월 1일 예측  10월 2일 예측  10월 3일 예측  10월 4일 예측\n",
              "0    고양시   9.527617   9.551300   9.459476   9.782426   9.555510\n",
              "1    평택시   1.162909   1.339653   1.958205   1.810958   0.901536\n",
              "2    부천시   3.129594   2.754975   2.555758   2.531941   2.233509\n",
              "3    수원시   1.323410   1.156042   1.044469   1.156081   0.658482\n",
              "4    구리시   0.008220  -0.134213  -0.020301  -0.077285   0.185843\n",
              "5    시흥시   1.012648   1.185181   0.986503   0.987714   0.881433\n",
              "6    김포시   0.142650  -0.001262   0.027520   0.171431  -0.167282\n",
              "7    이천시  -0.097564   0.312942   0.137012  -0.009596  -0.151508\n",
              "8    포천시   0.317668  -0.087185   0.086326  -0.231773   0.056697\n",
              "9    안양시   0.006727   0.087445   0.141230  -0.101258   0.127682\n",
              "10   용인시   1.487456   1.662997   2.072611   1.721490   1.302787\n",
              "11   파주시  -0.019550  -0.220063  -0.105490   0.180947   0.232517\n",
              "12  남양주시  -0.210516  -0.062602   0.440353  -0.033058  -0.324275\n",
              "13   성남시   1.780776   1.952422   1.780830   1.666443   1.343077\n",
              "14   과천시   0.059407  -0.030175  -0.060035   0.029546  -0.087707\n",
              "15   광명시   0.022698  -0.093317  -0.180339   0.109554   0.015990\n",
              "16   화성시   0.015803   0.075187   0.193964   0.164258  -0.216850\n",
              "17   오산시  -0.071750   0.074704   0.016123   0.016122  -0.069597\n",
              "18   군포시   0.064099  -0.133404   0.289743   0.035817  -0.106556\n",
              "19   의왕시  -0.059559  -0.059559   0.029048   0.176725  -0.029153\n",
              "20   안산시   0.005594   0.529501  -0.110788  -0.023455  -0.189544\n",
              "21   광주시   0.099787  -0.054470   0.192348   0.068943  -0.141984\n",
              "22   안성시   0.062385  -0.025252  -0.083677   0.033172   0.032116\n",
              "23  의정부시   0.253217   0.196685  -0.057609  -0.001134  -0.337473\n",
              "24   하남시  -0.106899   0.153474   0.124528  -0.078011   0.064270\n",
              "25  동두천시   0.050356  -0.037294  -0.037294   0.021139  -0.036354\n",
              "26   양주시  -0.073863   0.097537   0.011837  -0.016730  -0.099154\n",
              "27   가평군   0.016887  -0.101557  -0.131165  -0.012715   0.073991\n",
              "28   양평군  -0.224706   0.037905   0.738246  -0.108061   0.004587\n",
              "29   여주시   0.024857  -0.004061  -0.032979   0.053764  -0.061963\n",
              "30   연천군  -0.037733   0.020769   0.254792  -0.067001  -0.066547"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G5b8nf2gWho9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "outputId": "9ba2d4f3-7a23-4fdf-85c5-27b5d09a6951"
      },
      "source": [
        "# 일별 경기도 전체 확진자 수 예측 (9월 30일 - 10월 4일)\n",
        "for i in range(1,6):\n",
        "  print(sum(d.iloc[:,i]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "19.67262318255671\n",
            "20.144307761569422\n",
            "21.72124181990187\n",
            "19.958394523506225\n",
            "15.584081610002233\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}