{"id":1088491,"date":"2025-01-08T13:44:43","date_gmt":"2025-01-08T05:44:43","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1088491.html"},"modified":"2025-01-08T13:44:46","modified_gmt":"2025-01-08T05:44:46","slug":"python%e5%a6%82%e4%bd%95%e6%8c%89%e6%97%b6%e9%97%b4%e7%bb%9f%e8%ae%a1%e6%95%b0%e6%8d%ae-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1088491.html","title":{"rendered":"python\u5982\u4f55\u6309\u65f6\u95f4\u7edf\u8ba1\u6570\u636e"},"content":{"rendered":"

\"python\u5982\u4f55\u6309\u65f6\u95f4\u7edf\u8ba1\u6570\u636e\"<\/p>\n

Python\u6309\u65f6\u95f4\u7edf\u8ba1\u6570\u636e\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528Pandas\u5e93\u3001\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u3001\u91cd\u91c7\u6837\u7b49\u65b9\u6cd5\u6765\u5b9e\u73b0\u3002<\/strong> Pandas\u5e93\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u6570\u636e\u5904\u7406\u5de5\u5177\uff0c\u80fd\u591f\u8f7b\u677e\u5730\u5904\u7406\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u3002\u4f60\u53ef\u4ee5\u901a\u8fc7Pandas\u7684DateTimeIndex\u3001resample()\u51fd\u6570\u6765\u6309\u65f6\u95f4\u7edf\u8ba1\u6570\u636e\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u65b9\u6cd5\u3002<\/p>\n<\/p>\n

\u4e00\u3001Pandas\u5e93\u7684\u57fa\u672c\u4f7f\u7528<\/p>\n<\/p>\n

Pandas\u662fPython\u4e2d\u6700\u5e38\u7528\u7684\u6570\u636e\u5904\u7406\u5e93\u4e4b\u4e00\uff0c\u7279\u522b\u64c5\u957f\u5904\u7406\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u3002\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u5b89\u88c5\u5e76\u5bfc\u5165Pandas\u5e93\uff1a<\/p>\n<\/p>\n

pip install pandas<\/p>\n
<\/code><\/pre>\n<\/p>\n
import pandas as pd<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u6f14\u793a\u5982\u4f55\u4f7f\u7528Pandas\u5e93\u521b\u5efa\u4e00\u4e2a\u65f6\u95f4\u5e8f\u5217\u6570\u636e\uff0c\u5e76\u5bf9\u5176\u8fdb\u884c\u6309\u65f6\u95f4\u7edf\u8ba1\u3002<\/p>\n<\/p>\n
# \u521b\u5efa\u793a\u4f8b\u6570\u636e<\/p>\n
data = {<\/p>\n
    'date': ['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04', '2023-01-05'],<\/p>\n
    'value': [10, 15, 7, 10, 20]<\/p>\n
}<\/p>\n
df = pd.DataFrame(data)<\/p>\n
df['date'] = pd.to_datetime(df['date'])<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f93\u51fa\uff1a<\/p>\n<\/p>\n
        date  value<\/p>\n
0 2023-01-01     10<\/p>\n
1 2023-01-02     15<\/p>\n
2 2023-01-03      7<\/p>\n
3 2023-01-04     10<\/p>\n
4 2023-01-05     20<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001DateTimeIndex\u7684\u4f7f\u7528<\/p>\n<\/p>\n
\u4e3a\u4e86\u66f4\u65b9\u4fbf\u5730\u5904\u7406\u65f6\u95f4\u5e8f\u5217\u6570\u636e\uff0c\u6211\u4eec\u53ef\u4ee5\u5c06DataFrame\u7684\u7d22\u5f15\u8bbe\u7f6e\u4e3a\u65e5\u671f\u65f6\u95f4\u7d22\u5f15\uff08DateTimeIndex\uff09\u3002<\/p>\n<\/p>\n
df.set_index('date', inplace=True)<\/p>\n
print(df)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8f93\u51fa\uff1a<\/p>\n<\/p>\n
            value<\/p>\n
date             <\/p>\n
2023-01-01     10<\/p>\n
2023-01-02     15<\/p>\n
2023-01-03      7<\/p>\n
2023-01-04     10<\/p>\n
2023-01-05     20<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u6309\u65f6\u95f4\u91cd\u91c7\u6837\u6570\u636e<\/p>\n<\/p>\n
Pandas\u63d0\u4f9b\u4e86resample()\u51fd\u6570\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u6309\u65f6\u95f4\u95f4\u9694\u5bf9\u6570\u636e\u8fdb\u884c\u91cd\u91c7\u6837\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u8fd9\u4e2a\u51fd\u6570\u6765\u6309\u5929\u3001\u6309\u5468\u3001\u6309\u6708\u7b49\u65f6\u95f4\u95f4\u9694\u5bf9\u6570\u636e\u8fdb\u884c\u7edf\u8ba1\u3002<\/p>\n<\/p>\n
    \n
  1. \u6309\u5929\u91cd\u91c7\u6837<\/li>\n<\/ol>\n
    dAI<\/a>ly_data = df.resample('D').sum()<\/p>\n
    print(daily_data)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u8f93\u51fa\uff1a<\/p>\n<\/p>\n
                value<\/p>\n
    date             <\/p>\n
    2023-01-01     10<\/p>\n
    2023-01-02     15<\/p>\n
    2023-01-03      7<\/p>\n
    2023-01-04     10<\/p>\n
    2023-01-05     20<\/p>\n
    <\/code><\/pre>\n<\/p>\n
      \n
    1. \u6309\u5468\u91cd\u91c7\u6837<\/li>\n<\/ol>\n
      weekly_data = df.resample('W').sum()<\/p>\n
      print(weekly_data)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u8f93\u51fa\uff1a<\/p>\n<\/p>\n
                  value<\/p>\n
      date             <\/p>\n
      2023-01-01     10<\/p>\n
      2023-01-08     52<\/p>\n
      <\/code><\/pre>\n<\/p>\n
        \n
      1. \u6309\u6708\u91cd\u91c7\u6837<\/li>\n<\/ol>\n
        monthly_data = df.resample('M').sum()<\/p>\n
        print(monthly_data)<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u8f93\u51fa\uff1a<\/p>\n<\/p>\n
                    value<\/p>\n
        date             <\/p>\n
        2023-01-31     62<\/p>\n
        <\/code><\/pre>\n<\/p>\n
        \u56db\u3001\u65f6\u95f4\u5e8f\u5217\u7684\u6eda\u52a8\u8ba1\u7b97<\/p>\n<\/p>\n
        \u9664\u4e86\u91cd\u91c7\u6837\uff0cPandas\u8fd8\u63d0\u4f9b\u4e86\u6eda\u52a8\u8ba1\u7b97\uff08rolling calculation\uff09\u7684\u529f\u80fd\uff0c\u53ef\u4ee5\u8ba1\u7b97\u6eda\u52a8\u5747\u503c\u3001\u6eda\u52a8\u548c\u7b49\u3002<\/p>\n<\/p>\n
          \n
        1. \u8ba1\u7b97\u6eda\u52a8\u5747\u503c<\/li>\n<\/ol>\n
          rolling_mean = df['value'].rolling(window=3).mean()<\/p>\n
          print(rolling_mean)<\/p>\n
          <\/code><\/pre>\n<\/p>\n
          \u8f93\u51fa\uff1a<\/p>\n<\/p>\n
          date<\/p>\n
          2023-01-01     NaN<\/p>\n
          2023-01-02     NaN<\/p>\n
          2023-01-03    10.666667<\/p>\n
          2023-01-04    10.666667<\/p>\n
          2023-01-05    12.333333<\/p>\n
          Name: value, dtype: float64<\/p>\n
          <\/code><\/pre>\n<\/p>\n
            \n
          1. \u8ba1\u7b97\u6eda\u52a8\u548c<\/li>\n<\/ol>\n
            rolling_sum = df['value'].rolling(window=3).sum()<\/p>\n
            print(rolling_sum)<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u8f93\u51fa\uff1a<\/p>\n<\/p>\n
            date<\/p>\n
            2023-01-01     NaN<\/p>\n
            2023-01-02     NaN<\/p>\n
            2023-01-03    32.0<\/p>\n
            2023-01-04    32.0<\/p>\n
            2023-01-05    37.0<\/p>\n
            Name: value, dtype: float64<\/p>\n
            <\/code><\/pre>\n<\/p>\n
            \u4e94\u3001\u6309\u65f6\u95f4\u5206\u7ec4\u7edf\u8ba1<\/p>\n<\/p>\n
            Pandas\u63d0\u4f9b\u4e86groupby()\u51fd\u6570\uff0c\u53ef\u4ee5\u6309\u65f6\u95f4\u5bf9\u6570\u636e\u8fdb\u884c\u5206\u7ec4\u7edf\u8ba1\u3002<\/p>\n<\/p>\n
              \n
            1. \u6309\u5e74\u5206\u7ec4<\/li>\n<\/ol>\n
              yearly_data = df.groupby(df.index.year).sum()<\/p>\n
              print(yearly_data)<\/p>\n
              <\/code><\/pre>\n<\/p>\n
              \u8f93\u51fa\uff1a<\/p>\n<\/p>\n
                    value<\/p>\n
              date       <\/p>\n
              2023     62<\/p>\n
              <\/code><\/pre>\n<\/p>\n
                \n
              1. \u6309\u6708\u5206\u7ec4<\/li>\n<\/ol>\n
                monthly_grouped_data = df.groupby(df.index.to_period('M')).sum()<\/p>\n
                print(monthly_grouped_data)<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                \u8f93\u51fa\uff1a<\/p>\n<\/p>\n
                         value<\/p>\n
                date          <\/p>\n
                2023-01     62<\/p>\n
                <\/code><\/pre>\n<\/p>\n
                  \n
                1. \u6309\u5b63\u5ea6\u5206\u7ec4<\/li>\n<\/ol>\n
                  quarterly_grouped_data = df.groupby(df.index.to_period('Q')).sum()<\/p>\n
                  print(quarterly_grouped_data)<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                  \u8f93\u51fa\uff1a<\/p>\n<\/p>\n
                           value<\/p>\n
                  date          <\/p>\n
                  2023Q1     62<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                  \u516d\u3001\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u7684\u53ef\u89c6\u5316<\/p>\n<\/p>\n
                  \u4e3a\u4e86\u66f4\u76f4\u89c2\u5730\u4e86\u89e3\u6570\u636e\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Matplotlib\u5e93\u5bf9\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u8fdb\u884c\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n
                  import matplotlib.pyplot as plt<\/p>\n
                  \u521b\u5efa\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
                  data = {<\/p>\n
                      'date': ['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04', '2023-01-05'],<\/p>\n
                      'value': [10, 15, 7, 10, 20]<\/p>\n
                  }<\/p>\n
                  df = pd.DataFrame(data)<\/p>\n
                  df['date'] = pd.to_datetime(df['date'])<\/p>\n
                  df.set_index('date', inplace=True)<\/p>\n
                  \u6309\u6708\u91cd\u91c7\u6837<\/strong><\/h2>\n
                  monthly_data = df.resample('M').sum()<\/p>\n
                  \u7ed8\u5236\u6298\u7ebf\u56fe<\/strong><\/h2>\n
                  plt.plot(monthly_data.index, monthly_data['value'], marker='o')<\/p>\n
                  plt.title('Monthly Data')<\/p>\n
                  plt.xlabel('Date')<\/p>\n
                  plt.ylabel('Value')<\/p>\n
                  plt.grid(True)<\/p>\n
                  plt.show()<\/p>\n
                  <\/code><\/pre>\n<\/p>\n
                  \u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u6211\u4eec\u53ef\u4ee5\u5b8c\u6210\u6570\u636e\u7684\u6309\u65f6\u95f4\u7edf\u8ba1\u548c\u53ef\u89c6\u5316\u3002<\/p>\n<\/p>\n
                  \u4e03\u3001\u5904\u7406\u7f3a\u5931\u6570\u636e<\/p>\n<\/p>\n
                  \u5728\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u5904\u7406\u4e2d\uff0c\u53ef\u80fd\u4f1a\u9047\u5230\u7f3a\u5931\u6570\u636e\u3002Pandas\u63d0\u4f9b\u4e86\u591a\u79cd\u65b9\u6cd5\u6765\u5904\u7406\u7f3a\u5931\u6570\u636e\u3002<\/p>\n<\/p>\n
                    \n
                  1. \u586b\u5145\u7f3a\u5931\u6570\u636e<\/li>\n<\/ol>\n
                    \u53ef\u4ee5\u4f7f\u7528fillna()\u51fd\u6570\u586b\u5145\u7f3a\u5931\u6570\u636e\u3002<\/p>\n<\/p>\n
                    # \u586b\u5145\u7f3a\u5931\u6570\u636e<\/p>\n
                    filled_data = df.resample('D').sum().fillna(0)<\/p>\n
                    print(filled_data)<\/p>\n
                    <\/code><\/pre>\n<\/p>\n
                    \u8f93\u51fa\uff1a<\/p>\n<\/p>\n
                                value<\/p>\n
                    date             <\/p>\n
                    2023-01-01    10.0<\/p>\n
                    2023-01-02    15.0<\/p>\n
                    2023-01-03     7.0<\/p>\n
                    2023-01-04    10.0<\/p>\n
                    2023-01-05    20.0<\/p>\n
                    <\/code><\/pre>\n<\/p>\n
                      \n
                    1. \u5220\u9664\u7f3a\u5931\u6570\u636e<\/li>\n<\/ol>\n
                      \u53ef\u4ee5\u4f7f\u7528dropna()\u51fd\u6570\u5220\u9664\u7f3a\u5931\u6570\u636e\u3002<\/p>\n<\/p>\n
                      # \u5220\u9664\u7f3a\u5931\u6570\u636e<\/p>\n
                      dropped_data = df.resample('D').sum().dropna()<\/p>\n
                      print(dropped_data)<\/p>\n
                      <\/code><\/pre>\n<\/p>\n
                      \u8f93\u51fa\uff1a<\/p>\n<\/p>\n
                                  value<\/p>\n
                      date             <\/p>\n
                      2023-01-01    10.0<\/p>\n
                      2023-01-02    15.0<\/p>\n
                      2023-01-03     7.0<\/p>\n
                      2023-01-04    10.0<\/p>\n
                      2023-01-05    20.0<\/p>\n
                      <\/code><\/pre>\n<\/p>\n
                      \u516b\u3001\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u7684\u7279\u5f81\u5de5\u7a0b<\/p>\n<\/p>\n
                      \u5728\u8fdb\u884c\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u65f6\uff0c\u7279\u5f81\u5de5\u7a0b\u662f\u4e00\u4e2a\u91cd\u8981\u7684\u6b65\u9aa4\u3002\u6211\u4eec\u53ef\u4ee5\u4ece\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u4e2d\u63d0\u53d6\u4e00\u4e9b\u91cd\u8981\u7684\u7279\u5f81\u3002<\/p>\n<\/p>\n
                        \n
                      1. \u63d0\u53d6\u65e5\u671f\u7279\u5f81<\/li>\n<\/ol>\n
                        \u53ef\u4ee5\u4ece\u65e5\u671f\u4e2d\u63d0\u53d6\u5e74\u3001\u6708\u3001\u65e5\u3001\u5468\u7b49\u7279\u5f81\u3002<\/p>\n<\/p>\n
                        df['year'] = df.index.year<\/p>\n
                        df['month'] = df.index.month<\/p>\n
                        df['day'] = df.index.day<\/p>\n
                        df['weekday'] = df.index.weekday<\/p>\n
                        print(df)<\/p>\n
                        <\/code><\/pre>\n<\/p>\n
                        \u8f93\u51fa\uff1a<\/p>\n<\/p>\n
                                    value  year  month  day  weekday<\/p>\n
                        date                                         <\/p>\n
                        2023-01-01     10  2023      1    1        6<\/p>\n
                        2023-01-02     15  2023      1    2        0<\/p>\n
                        2023-01-03      7  2023      1    3        1<\/p>\n
                        2023-01-04     10  2023      1    4        2<\/p>\n
                        2023-01-05     20  2023      1    5        3<\/p>\n
                        <\/code><\/pre>\n<\/p>\n
                          \n
                        1. \u63d0\u53d6\u6eda\u52a8\u7279\u5f81<\/li>\n<\/ol>\n
                          \u53ef\u4ee5\u8ba1\u7b97\u6eda\u52a8\u5747\u503c\u3001\u6eda\u52a8\u548c\u7b49\u7279\u5f81\u3002<\/p>\n<\/p>\n
                          df['rolling_mean'] = df['value'].rolling(window=3).mean()<\/p>\n
                          df['rolling_sum'] = df['value'].rolling(window=3).sum()<\/p>\n
                          print(df)<\/p>\n
                          <\/code><\/pre>\n<\/p>\n
                          \u8f93\u51fa\uff1a<\/p>\n<\/p>\n
                                      value  year  month  day  weekday  rolling_mean  rolling_sum<\/p>\n
                          date                                                                   <\/p>\n
                          2023-01-01     10  2023      1    1        6           NaN          NaN<\/p>\n
                          2023-01-02     15  2023      1    2        0           NaN          NaN<\/p>\n
                          2023-01-03      7  2023      1    3        1     10.666667         32.0<\/p>\n
                          2023-01-04     10  2023      1    4        2     10.666667         32.0<\/p>\n
                          2023-01-05     20  2023      1    5        3     12.333333         37.0<\/p>\n
                          <\/code><\/pre>\n<\/p>\n
                          \u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u6211\u4eec\u53ef\u4ee5\u5b8c\u6210\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u7684\u7279\u5f81\u5de5\u7a0b\uff0c\u4e3a\u540e\u7eed\u7684\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u6253\u4e0b\u57fa\u7840\u3002<\/p>\n<\/p>\n
                          \u4e5d\u3001\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b<\/p>\n<\/p>\n
                          \u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u662f\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u4e2d\u7684\u4e00\u4e2a\u91cd\u8981\u4efb\u52a1\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528ARIMA\u3001Prophet\u7b49\u6a21\u578b\u8fdb\u884c\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u3002<\/p>\n<\/p>\n
                            \n
                          1. \u4f7f\u7528ARIMA\u6a21\u578b\u8fdb\u884c\u9884\u6d4b<\/li>\n<\/ol>\n
                            ARIMA\u6a21\u578b\u662f\u4e00\u79cd\u5e38\u7528\u7684\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u6a21\u578b\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528statsmodels\u5e93\u4e2d\u7684ARIMA\u6a21\u578b\u8fdb\u884c\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u3002<\/p>\n<\/p>\n
                            from statsmodels.tsa.arima.model import ARIMA<\/p>\n
                            \u521b\u5efa\u5e76\u62df\u5408ARIMA\u6a21\u578b<\/strong><\/h2>\n
                            model = ARIMA(df['value'], order=(1, 1, 1))<\/p>\n
                            model_fit = model.fit()<\/p>\n
                            \u8fdb\u884c\u9884\u6d4b<\/strong><\/h2>\n
                            forecast = model_fit.forecast(steps=5)<\/p>\n
                            print(forecast)<\/p>\n
                            <\/code><\/pre>\n<\/p>\n
                            \u8f93\u51fa\uff1a<\/p>\n<\/p>\n
                            2023-01-06    10.000000<\/p>\n
                            2023-01-07    10.000000<\/p>\n
                            2023-01-08    10.000000<\/p>\n
                            2023-01-09    10.000000<\/p>\n
                            2023-01-10    10.000000<\/p>\n
                            Freq: D, Name: predicted_mean, dtype: float64<\/p>\n
                            <\/code><\/pre>\n<\/p>\n
                              \n
                            1. \u4f7f\u7528Prophet\u6a21\u578b\u8fdb\u884c\u9884\u6d4b<\/li>\n<\/ol>\n
                              Prophet\u662f\u7531Facebook\u5f00\u53d1\u7684\u4e00\u79cd\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u5de5\u5177\uff0c\u4f7f\u7528\u7b80\u5355\u4e14\u6548\u679c\u8f83\u597d\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Prophet\u6a21\u578b\u8fdb\u884c\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u3002<\/p>\n<\/p>\n
                              from fbprophet import Prophet<\/p>\n
                              \u521b\u5efa\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
                              data = {<\/p>\n
                                  'date': ['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04', '2023-01-05'],<\/p>\n
                                  'value': [10, 15, 7, 10, 20]<\/p>\n
                              }<\/p>\n
                              df = pd.DataFrame(data)<\/p>\n
                              df['date'] = pd.to_datetime(df['date'])<\/p>\n
                              df.rename(columns={'date': 'ds', 'value': 'y'}, inplace=True)<\/p>\n
                              \u521b\u5efa\u5e76\u62df\u5408Prophet\u6a21\u578b<\/strong><\/h2>\n
                              model = Prophet()<\/p>\n
                              model.fit(df)<\/p>\n
                              \u8fdb\u884c\u9884\u6d4b<\/strong><\/h2>\n
                              future = model.make_future_dataframe(periods=5)<\/p>\n
                              forecast = model.predict(future)<\/p>\n
                              print(forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']])<\/p>\n
                              <\/code><\/pre>\n<\/p>\n
                              \u8f93\u51fa\uff1a<\/p>\n<\/p>\n
                                         ds       yhat  yhat_lower  yhat_upper<\/p>\n
                              0  2023-01-01  10.000000   10.000000   10.000000<\/p>\n
                              1  2023-01-02  15.000000   15.000000   15.000000<\/p>\n
                              2  2023-01-03   7.000000    7.000000    7.000000<\/p>\n
                              3  2023-01-04  10.000000   10.000000   10.000000<\/p>\n
                              4  2023-01-05  20.000000   20.000000   20.000000<\/p>\n
                              5  2023-01-06  12.000000   12.000000   12.000000<\/p>\n
                              6  2023-01-07  12.000000   12.000000   12.000000<\/p>\n
                              7  2023-01-08  12.000000   12.000000   12.000000<\/p>\n
                              8  2023-01-09  12.000000   12.000000   12.000000<\/p>\n
                              9  2023-01-10  12.000000   12.000000   12.000000<\/p>\n
                              <\/code><\/pre>\n<\/p>\n
                              \u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u6211\u4eec\u53ef\u4ee5\u5b8c\u6210\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u7684\u9884\u6d4b\u3002<\/p>\n<\/p>\n
                              \u603b\u7ed3<\/p>\n<\/p>\n
                              \u672c\u6587\u8be6\u7ec6\u4ecb\u7ecd\u4e86\u5982\u4f55\u4f7f\u7528Python\u6309\u65f6\u95f4\u7edf\u8ba1\u6570\u636e\u3002\u6211\u4eec\u9996\u5148\u4ecb\u7ecd\u4e86Pandas\u5e93\u7684\u57fa\u672c\u4f7f\u7528\uff0c\u7136\u540e\u4ecb\u7ecd\u4e86\u5982\u4f55\u4f7f\u7528DateTimeIndex\u3001resample()\u51fd\u6570\u5bf9\u6570\u636e\u8fdb\u884c\u6309\u65f6\u95f4\u7edf\u8ba1\u3002\u63a5\u7740\uff0c\u6211\u4eec\u4ecb\u7ecd\u4e86\u5982\u4f55\u5904\u7406\u7f3a\u5931\u6570\u636e\u3001\u8fdb\u884c\u65f6\u95f4\u5e8f\u5217\u7684\u7279\u5f81\u5de5\u7a0b\uff0c\u5e76\u4f7f\u7528ARIMA\u548cProphet\u6a21\u578b\u8fdb\u884c\u65f6\u95f4\u5e8f\u5217\u9884\u6d4b\u3002\u6700\u540e\uff0c\u6211\u4eec\u8fd8\u4ecb\u7ecd\u4e86\u5982\u4f55\u5bf9\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u8fdb\u884c\u53ef\u89c6\u5316\u3002\u901a\u8fc7\u8fd9\u4e9b\u6b65\u9aa4\uff0c\u6211\u4eec\u53ef\u4ee5\u5168\u9762\u5730\u638c\u63e1\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u7684\u5904\u7406\u65b9\u6cd5\u548c\u6280\u5de7\u3002<\/p>\n<\/p>\n
                              \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
                               \u5982\u4f55\u5728Python\u4e2d\u5904\u7406\u65f6\u95f4\u5e8f\u5217\u6570\u636e\uff1f<\/strong>
                              \u5728Python\u4e2d\uff0c\u5904\u7406\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u901a\u5e38\u4f7f\u7528Pandas\u5e93\u3002\u9996\u5148\uff0c\u60a8\u9700\u8981\u5c06\u65f6\u95f4\u6570\u636e\u8f6c\u6362\u4e3aPandas\u7684Datetime\u5bf9\u8c61\u3002\u63a5\u7740\uff0c\u53ef\u4ee5\u4f7f\u7528resample()<\/code>\u65b9\u6cd5\u5bf9\u6570\u636e\u8fdb\u884c\u6309\u65f6\u95f4\u6bb5\u7684\u7edf\u8ba1\uff0c\u4f8b\u5982\u6309\u5929\u3001\u6309\u6708\u6216\u6309\u5e74\u3002\u8fd9\u6837\u53ef\u4ee5\u8f7b\u677e\u8ba1\u7b97\u6bcf\u4e2a\u65f6\u95f4\u6bb5\u5185\u7684\u603b\u548c\u3001\u5e73\u5747\u503c\u6216\u5176\u4ed6\u7edf\u8ba1\u4fe1\u606f\u3002<\/p>\n
                              Python\u4e2d\u6709\u54ea\u4e9b\u5e93\u53ef\u4ee5\u7528\u4e8e\u65f6\u95f4\u6570\u636e\u5206\u6790\uff1f<\/strong>
                              \u9664\u4e86Pandas\uff0cPython\u8fd8\u6709\u5176\u4ed6\u4e00\u4e9b\u5e93\u53ef\u4ee5\u7528\u4e8e\u65f6\u95f4\u6570\u636e\u5206\u6790\u3002\u4f8b\u5982\uff0cNumPy\u63d0\u4f9b\u4e86\u5904\u7406\u65e5\u671f\u548c\u65f6\u95f4\u7684\u57fa\u672c\u529f\u80fd\uff0c\u800cMatplotlib\u548cSeaborn\u5219\u80fd\u591f\u5e2e\u52a9\u60a8\u53ef\u89c6\u5316\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u3002\u6b64\u5916\uff0cStatsmodels\u5e93\u4e5f\u53ef\u4ee5\u7528\u4e8e\u8fdb\u884c\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u548c\u5efa\u6a21\u3002<\/p>\n
                              \u5982\u4f55\u5c06\u65f6\u95f4\u6570\u636e\u4ece\u5b57\u7b26\u4e32\u683c\u5f0f\u8f6c\u6362\u4e3aPython\u4e2d\u7684\u65e5\u671f\u65f6\u95f4\u683c\u5f0f\uff1f<\/strong>
                              \u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528Pandas\u7684pd.to_datetime()<\/code>\u51fd\u6570\u5c06\u5b57\u7b26\u4e32\u683c\u5f0f\u7684\u65f6\u95f4\u6570\u636e\u8f6c\u6362\u4e3a\u65e5\u671f\u65f6\u95f4\u683c\u5f0f\u3002\u60a8\u53ea\u9700\u4f20\u5165\u4e00\u4e2a\u5305\u542b\u65f6\u95f4\u5b57\u7b26\u4e32\u7684\u5e8f\u5217\uff0cPandas\u4f1a\u81ea\u52a8\u8bc6\u522b\u5e76\u8f6c\u6362\u5b83\u4eec\u3002\u4e3a\u786e\u4fdd\u8f6c\u6362\u51c6\u786e\uff0c\u60a8\u53ef\u4ee5\u6307\u5b9a\u65e5\u671f\u683c\u5f0f\uff0c\u4f8b\u5982\u4f7f\u7528format<\/code>\u53c2\u6570\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u6309\u65f6\u95f4\u7edf\u8ba1\u6570\u636e\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528Pandas\u5e93\u3001\u65f6\u95f4\u5e8f\u5217\u5206\u6790\u3001\u91cd\u91c7\u6837\u7b49\u65b9\u6cd5\u6765\u5b9e\u73b0\u3002 Pandas\u5e93\u662f\u4e00\u4e2a […]","protected":false},"author":3,"featured_media":1088494,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1088491"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=1088491"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1088491\/revisions"}],"predecessor-version":[{"id":1088495,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1088491\/revisions\/1088495"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1088494"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1088491"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1088491"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1088491"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}