Python 如何为超过小时数的数据分配小时数？

考虑到以下几点：

timeline = pd.date_range(start="2027-01-01",
              end="2061-01-01",
              freq="H")
timeline = timeline[:-1]

df1 = pd.DataFrame()

for i in range(0, 34):
    df2 = pd.DataFrame()
    df2['value'] = np.random.randint(1, 6, 8900)
    df2['year'] = 2027 + i
    df1 = pd.concat([df1, df2])

df1['Row'] = df1.groupby(['year']).cumcount()
timeline = timeline.to_frame()
timeline = timeline.rename(columns={(0):'date'})
timeline['year'] = timeline.date.dt.year
timeline['Row'] = timeline.groupby(['year']).cumcount()

请注意，8900始终大于366*24。目标是将时间线和df1结合起来，以便使用前n行填充时间线。我们省略了当年的后续行，并继续到下一年

我遇到的问题是，并非所有年份的小时数都相同，因为有些年份是闰年，这相当麻烦。我想知道是否有一个有效的方法来处理这个问题

考虑到每年不同时间的复杂性，是否有执行合并的方法？

代码

df1 = df1.reset_index(drop=True)
timeline = timeline.to_frame()
timeline = timeline.rename(columns={(0):'date'})
timeline['tlyear'] = timeline.date.dt.year
timeline = timeline.reset_index(drop=True)
pd.concat([timeline,df1], join='inner', axis=1).drop('tlyear',1)

timeline = pd.date_range(start="2027-01-01",
              end="2061-01-01",
              freq="H")
timeline = timeline[:-1]
df1 = pd.DataFrame()

for i in range(0, 34):
    df2 = pd.DataFrame()
    df2['value'] = np.random.randint(1, 6, 8900)
    df2['year'] = 2027 + i
    df1 = pd.concat([df1, df2])
df1 = df1.reset_index(drop=True)

timeline = timeline.to_frame()
timeline = timeline.rename(columns={(0):'date'})
timeline['tlyear'] = timeline.date.dt.year
timeline = timeline.reset_index(drop=True)

pd.concat([timeline,df1], join='inner', axis=1).drop('tlyear',1)

    date                value   year
0   2027-01-01 00:00:00 5       2027
1   2027-01-01 01:00:00 2       2027
2   2027-01-01 02:00:00 3       2027
3   2027-01-01 03:00:00 4       2027
4   2027-01-01 04:00:00 1       2027
... ... ... ...
298051  2060-12-31 19:00:00 1   2060
298052  2060-12-31 20:00:00 3   2060
298053  2060-12-31 21:00:00 2   2060
298054  2060-12-31 22:00:00 1   2060
298055  2060-12-31 23:00:00 3   2060

完整代码

df1 = df1.reset_index(drop=True)
timeline = timeline.to_frame()
timeline = timeline.rename(columns={(0):'date'})
timeline['tlyear'] = timeline.date.dt.year
timeline = timeline.reset_index(drop=True)
pd.concat([timeline,df1], join='inner', axis=1).drop('tlyear',1)

timeline = pd.date_range(start="2027-01-01",
              end="2061-01-01",
              freq="H")
timeline = timeline[:-1]
df1 = pd.DataFrame()

for i in range(0, 34):
    df2 = pd.DataFrame()
    df2['value'] = np.random.randint(1, 6, 8900)
    df2['year'] = 2027 + i
    df1 = pd.concat([df1, df2])
df1 = df1.reset_index(drop=True)

timeline = timeline.to_frame()
timeline = timeline.rename(columns={(0):'date'})
timeline['tlyear'] = timeline.date.dt.year
timeline = timeline.reset_index(drop=True)

pd.concat([timeline,df1], join='inner', axis=1).drop('tlyear',1)

    date                value   year
0   2027-01-01 00:00:00 5       2027
1   2027-01-01 01:00:00 2       2027
2   2027-01-01 02:00:00 3       2027
3   2027-01-01 03:00:00 4       2027
4   2027-01-01 04:00:00 1       2027
... ... ... ...
298051  2060-12-31 19:00:00 1   2060
298052  2060-12-31 20:00:00 3   2060
298053  2060-12-31 21:00:00 2   2060
298054  2060-12-31 22:00:00 1   2060
298055  2060-12-31 23:00:00 3   2060

编辑

for i in range(0, 34):
    df2 = pd.DataFrame()
    df2['value'] = np.random.randint(1, 6, 8900)
    df2['year'] = 2027 + i
    df1 = pd.concat([df1, df2])
df1 = df1.reset_index(drop=True)

timeline = timeline.to_frame()
timeline = timeline.rename(columns={(0):'date'})
timeline['year'] = timeline.date.dt.year
timeline = timeline.reset_index(drop=True)

pd.merge_asof(df1, timeline, on='year', direction='nearest')

输出样本

df1 = df1.reset_index(drop=True)
timeline = timeline.to_frame()
timeline = timeline.rename(columns={(0):'date'})
timeline['tlyear'] = timeline.date.dt.year
timeline = timeline.reset_index(drop=True)
pd.concat([timeline,df1], join='inner', axis=1).drop('tlyear',1)

timeline = pd.date_range(start="2027-01-01",
              end="2061-01-01",
              freq="H")
timeline = timeline[:-1]
df1 = pd.DataFrame()

for i in range(0, 34):
    df2 = pd.DataFrame()
    df2['value'] = np.random.randint(1, 6, 8900)
    df2['year'] = 2027 + i
    df1 = pd.concat([df1, df2])
df1 = df1.reset_index(drop=True)

timeline = timeline.to_frame()
timeline = timeline.rename(columns={(0):'date'})
timeline['tlyear'] = timeline.date.dt.year
timeline = timeline.reset_index(drop=True)

pd.concat([timeline,df1], join='inner', axis=1).drop('tlyear',1)

    date                value   year
0   2027-01-01 00:00:00 5       2027
1   2027-01-01 01:00:00 2       2027
2   2027-01-01 02:00:00 3       2027
3   2027-01-01 03:00:00 4       2027
4   2027-01-01 04:00:00 1       2027
... ... ... ...
298051  2060-12-31 19:00:00 1   2060
298052  2060-12-31 20:00:00 3   2060
298053  2060-12-31 21:00:00 2   2060
298054  2060-12-31 22:00:00 1   2060
298055  2060-12-31 23:00:00 3   2060

---

我想到了一种稍微不同的方法，我们可以做以下几点：

timeline = pd.date_range(start="2027-01-01",
              end="2061-01-01",
              freq="H")
timeline = timeline[:-1]

df1 = pd.DataFrame()

for i in range(0, 34):
    df2 = pd.DataFrame()
    df2['value'] = np.random.randint(1, 6, 8900)
    df2['year'] = 2027 + i
    df1 = pd.concat([df1, df2])

df1['Row'] = df1.groupby(['year']).cumcount()
timeline = timeline.to_frame()
timeline = timeline.rename(columns={(0):'date'})
timeline['year'] = timeline.date.dt.year
timeline['Row'] = timeline.groupby(['year']).cumcount()

然后在它们上面合并：

result = timeline.merge(df1, on=['year', 'Row'])

---

我相信这将强制执行行顺序。

我有点担心键匹配的顺序。如果我们在某个非唯一键上进行连接，那么只有左表的第一行与第一行与第二行的匹配项合并。此外，匹配的第一行不会用于第一列的第二行。也许我弄错了，但是我在文档中找不到它，文档记录了这种行为。我已经编辑了代码，以便根据条件进行合并。请检查它是否有效。