将多个时间序列组合成一个数据帧

Question

我的任务是将许多时间序列组合成一个数据集。数据分块：具有日期和时间索引的时间序列，不同的名称，但重叠的日期和时间。合并时，我得到一个包含重复行的数据集。

我的代码：

>>> import pandas as pd
>>> s1 = pd.Series([1.1,1.2], index=pd.date_range("2000-01-01 00:00:00", freq="S", periods=2), name="id_1")
>>> s2 = pd.Series([1.3,1.4], index=pd.date_range("2000-01-01 00:00:02", freq="S", periods=2), name="id_1")
>>> s3 = pd.Series([2.1,2.2], index=pd.date_range("2000-01-01 00:00:00", freq="S", periods=2), name="id_2")
>>> s4 = pd.Series([2.3,2.4], index=pd.date_range("2000-01-01 00:00:02", freq="S", periods=2), name="id_2")
>>> df = pd.DataFrame()
>>> df.append([s1,s2,s3,s4])
      2000-01-01 00:00:00  2000-01-01 00:00:01  2000-01-01 00:00:02  2000-01-01 00:00:03
id_1                  1.1                  1.2                  NaN                  NaN
id_1                  NaN                  NaN                  1.3                  1.4
id_2                  2.1                  2.2                  NaN                  NaN
id_2                  NaN                  NaN                  2.3                  2.4

我希望数据集是这样的：

      2000-01-01 00:00:00  2000-01-01 00:00:01  2000-01-01 00:00:02  2000-01-01 00:00:03
id_1                  1.1                  1.2                  1.3                  1.4
id_2                  2.1                  2.2                  2.3                  2.4

Answer 1

functools.reduce

/shrug 想到的第一件事

from functools import reduce

reduce(pd.DataFrame.combine_first, map(pd.Series.to_frame, [s1, s2, s3, s4])).T

      2000-01-01 00:00:00  2000-01-01 00:00:01  2000-01-01 00:00:02  2000-01-01 00:00:03
id_1                  1.1                  1.2                  1.3                  1.4
id_2                  2.1                  2.2                  2.3                  2.4

---

重建数据

dat = {}
for s in [s1, s2, s3, s4]:
    for k, v in s.iteritems():
        dat.setdefault(k, {})[s.name] = v

pd.DataFrame(dat)

      2000-01-01 00:00:00  2000-01-01 00:00:01  2000-01-01 00:00:02  2000-01-01 00:00:03
id_1                  1.1                  1.2                  1.3                  1.4
id_2                  2.1                  2.2                  2.3                  2.4