我想利用一个邮资数据框架来跟踪一些市场数据,我将在交易日下载现场。
我要说的是,我要标出AAPL和GOOG的价格。 首先,我建立了一个数据框架:
prices = DataFrame(columns = [ AAPL , GOOG ])
请允许我说,AAPL的第一份数据点当时为t1和555.0。 之后,几秒以图2计算,全球测验组的价格为430 000美元。
当然可以做到:
prices[ AAPL ][t1] = 555.0
prices[ GOOG ][t2] = 430.0
Is there an easy/fast way in pandas to accomplish this though besides pulling the index, modifying it, reindexing the dataframe and then inserting each scalar price as it comes in?
列出<代码>_数值方法(如果大小改动,可检索新物体)。 但并不指望它会很快(与一条nes子相比):
In [7]: prices
Out[7]:
Empty DataFrame
Columns: array([AAPL, GOOG], dtype=object)
Index: array([], dtype=object)
In [8]: prices = prices.set_value(t1, AAPL , 5)
In [9]: prices
Out[9]:
AAPL GOOG
2012-04-12 18:02:28.178331 5 NaN
在某个时候添加一种方法,以便更高效地利用数据框架(NumPy确实有这方面的设施)。
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