Pandas，如何引用时间序列项目? [英] Pandas, How to reference Timeseries Items?

问题描述

我正在尝试处理一些股市数据.我有以下数据帧:

<预><代码>>>>股票代码<class 'pandas.core.frame.DataFrame'>日期时间索引:707 个条目，2010-01-04 00:00:00 到 2012-10-19 00:00:00数据列:打开 707 个非空值高 707 非空值低 707 非空值关闭 707 个非空值第 707 卷非空值调整关闭 707 个非空值数据类型:float64(5)、int64(1)

我将参考随机收盘价:

<预><代码>>>>股票代码 ['关闭'] [704]21.789999999999999

获取第 704 项日期的语法是什么?

同样，如何获得以下项在数组中的位置?:

<预><代码>>>>ticker.Close.min ()17.670000000000002

我知道这看起来很基本，但我花了很多时间搜索文档.如果它在那里，我绝对想念它.

解决方案

这应该能回答你的两个问题:

注意:如果你想要第 704 个元素，你应该使用703"作为索引从零开始.如您所见，df['A'].argmin() 也返回 1，即 df 中的第二行.

在[682]中:打印df乙丙2000-01-01 1.073247 -1.784255 0.1372622000-01-02 -0.797483 0.665392 0.6924292000-01-03 0.123751 0.532109 0.8142452000-01-04 1.045414 -0.687119 -0.4514372000-01-05 0.594588 0.240058 -0.8139542000-01-06 1.104193 0.765873 0.5272622000-01-07 -0.304374 -0.894570 -0.8466792000-01-08 -0.443329 -1.437305 -0.316648在 [683]: df.index[3]Out[683]:<时间戳:2000-01-04 00:00:00>在 [684]: df['A'].argmin()出[684]:1

I'm trying to work with some stock market data. I have the following DataFrame:

>>> ticker
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 707 entries, 2010-01-04 00:00:00 to 2012-10-19 00:00:00
Data columns:
Open         707  non-null values
High         707  non-null values
Low          707  non-null values
Close        707  non-null values
Volume       707  non-null values
Adj Close    707  non-null values
dtypes: float64(5), int64(1)

I'll reference a random closing price:

>>> ticker ['Close'] [704]
21.789999999999999

What's the syntax to get the date of that 704th item?

Similarly, how do I get the position in the array of the following item?:

>>> ticker.Close.min ()
17.670000000000002

I know this seems pretty basic, but I've spent a lot of time scouring the documentation. If it's there, I'm absolutely missing it.

解决方案

This should answer both your questions:

Note: if you want the 704th element, you should use "703" as index starts form zero. As you see df['A'].argmin() also returns 1, that is the second row in the df.

In [682]: print df
                   A         B         C
2000-01-01  1.073247 -1.784255  0.137262
2000-01-02 -0.797483  0.665392  0.692429
2000-01-03  0.123751  0.532109  0.814245
2000-01-04  1.045414 -0.687119 -0.451437
2000-01-05  0.594588  0.240058 -0.813954
2000-01-06  1.104193  0.765873  0.527262
2000-01-07 -0.304374 -0.894570 -0.846679
2000-01-08 -0.443329 -1.437305 -0.316648


In [683]: df.index[3]
Out[683]: <Timestamp: 2000-01-04 00:00:00>

In [684]: df['A'].argmin()
Out[684]: 1

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