Python 如何将 Pandas DataFrame 转换为 TimeSeries？
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How to convert a pandas DataFrame into a TimeSeries?
提问by morgan
I am looking for a way to convert a DataFrame to a TimeSeries without splitting the index and value columns. Any ideas? Thanks.
我正在寻找一种将 DataFrame 转换为 TimeSeries 而不拆分索引和值列的方法。有任何想法吗？谢谢。
In [20]: import pandas as pd
In [21]: import numpy as np
In [22]: dates = pd.date_range('20130101',periods=6)
In [23]: df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=list('ABCD'))
In [24]: df
Out[24]:
A B C D
2013-01-01 -0.119230 1.892838 0.843414 -0.482739
2013-01-02 1.204884 -0.942299 -0.521808 0.446309
2013-01-03 1.899832 0.460871 -1.491727 -0.647614
2013-01-04 1.126043 0.818145 0.159674 -1.490958
2013-01-05 0.113360 0.190421 -0.618656 0.976943
2013-01-06 -0.537863 -0.078802 0.197864 -1.414924
In [25]: pd.Series(df)
Out[25]:
0 A
1 B
2 C
3 D
dtype: object
回答by Jeff
Here is one possibility
这是一种可能性
[3]: df
Out[3]:
A B C D
2013-01-01 -0.024362 0.712035 -0.913923 0.755276
2013-01-02 2.624298 0.285546 0.142265 -0.047871
2013-01-03 1.315157 -0.333630 0.398759 -1.034859
2013-01-04 0.713141 -0.109539 0.263706 -0.588048
2013-01-05 -1.172163 -1.387645 -0.171854 -0.458660
2013-01-06 -0.192586 0.480023 -0.530907 -0.872709
In [4]: df.unstack()
Out[4]:
A 2013-01-01 -0.024362
2013-01-02 2.624298
2013-01-03 1.315157
2013-01-04 0.713141
2013-01-05 -1.172163
2013-01-06 -0.192586
B 2013-01-01 0.712035
2013-01-02 0.285546
2013-01-03 -0.333630
2013-01-04 -0.109539
2013-01-05 -1.387645
2013-01-06 0.480023
C 2013-01-01 -0.913923
2013-01-02 0.142265
2013-01-03 0.398759
2013-01-04 0.263706
2013-01-05 -0.171854
2013-01-06 -0.530907
D 2013-01-01 0.755276
2013-01-02 -0.047871
2013-01-03 -1.034859
2013-01-04 -0.588048
2013-01-05 -0.458660
2013-01-06 -0.872709
dtype: float64
回答by EngineeredE
I know this is late to the game here but a few points.
我知道这里的比赛已经晚了，但有几点。
Whether or not a DataFrameis considered a TimeSeriesis the type of index. In your case, your index is already a TimeSeries, so you are good to go. For more information on all the cool slicing you can do with a the pd.timeseries index, take a look at http://pandas.pydata.org/pandas-docs/stable/timeseries.html#datetime-indexing
是否将 aDataFrame视为 aTimeSeries是索引的类型。在您的情况下，您的索引已经是 a TimeSeries，所以您很高兴。有关您可以使用 pd.timeseries 索引进行的所有酷切片的更多信息，请查看http://pandas.pydata.org/pandas-docs/stable/timeseries.html#datetime-indexing
Now, others might arrive here because they have a column 'DateTime'that they want to make an index, in which case the answer is simple
现在，其他人可能会到达这里，因为他们有一列'DateTime'想要创建索引，在这种情况下，答案很简单
ts = df.set_index('DateTime')
