过滤 pandas 数据框 [英] Filter Pandas Data frame

问题描述

我有这个熊猫数据框：

 打开高价低价收盘价
 TimeStamp 
 2016-06 -23 10:00:00 586.76 594.00 585.54 589.94 478.176973 
 2016-06-23 11:00:00 589.94 595.49 588.23 592.63 448.689485 
 2016-06-23 12:00:00 592.63 592.63 1.50 581.13 527.816891 
 2016-06-23 13:00:00 581.13 586.33 578.58 580.96 728.424757 
  

当您可以看到其中一个值不正确。
因此，我想对其进行过滤并将其更改为最后5个值的平均值

有了这个

  df ['avg'] = df ['low']。rolling（5）.mean（）。shift（）
  

我明白了

 打开高低关闭成交量平均
 TimeStamp 
 2016-06-23 10:00:00 586.76 594.00 585.54 589.94 478.176973 573.326 
 2016-06-23 11:00:00 589.94 595.49 588.23 592.63 448.689485 578.438 
 2016 -06-23 12:00:00 592.63 592.63 1.50 581.13 527.816891 583.202 
 2016-06-23 13:00:00 581.13 586.33 578.58 580.96 728.424757 467.348 
  

现在我想将相同的平均值给低。
过滤器发现方差大于5的值。

  df.loc [（df ['高'] / df ['低']）> 5] ['低'] 
 
 
 $ b开盘高低平仓成交量平均
时间戳
 2016-06 -23 12:00:00 592.63 592.63 1.5 581.13 527.816891 583.202 
  

但是当我尝试给出值时，就行不通了。 ）> 5] ['low'] = df.loc [（df ['high'] / df ['low']）> 5] ['avg']

你能帮我吗？

解决方案

pandas'数据框基于索引，因此您只需要

  df.loc [（df ['high'] / df [ ''low']）> 5，'low'] = df.avg 
 df 
 Out [1331]：
开盘高低平仓成交量
 0 586.76 594.00 585.54 589.94 478.176973 NaN 
 1 589.94 595.49 588.23 592.63 448.689485 585.54 
 2 592.63 592.63 588.23 581.13 527.816891 588.23 
 3 581.13 586.33 578.58 580.96 728.424757 1.50 
  

I have this pandas dataframe:

                     open    high     low   close      volume
TimeStamp                                                      
2016-06-23 10:00:00  586.76  594.00  585.54  589.94  478.176973
2016-06-23 11:00:00  589.94  595.49  588.23  592.63  448.689485
2016-06-23 12:00:00  592.63  592.63    1.50  581.13  527.816891
2016-06-23 13:00:00  581.13  586.33  578.58  580.96  728.424757

As you can see one of the values is not ok. So I want to filter it and change it to the mean of the last 5 values

With this

df['avg']=df['low'].rolling(5).mean().shift()

I get this

                      open    high     low   close      volume      avg
TimeStamp                                                               
2016-06-23 10:00:00  586.76  594.00  585.54  589.94  478.176973  573.326
2016-06-23 11:00:00  589.94  595.49  588.23  592.63  448.689485  578.438
2016-06-23 12:00:00  592.63  592.63    1.50  581.13  527.816891  583.202
2016-06-23 13:00:00  581.13  586.33  578.58  580.96  728.424757  467.348

And now I want to give to the low the same value of avg. The filter finds value that has a "variance" bigger than 5.

df.loc[(df['high']/df['low'])>5]['low']


                       open    high  low   close      volume      avg
TimeStamp                                                            
2016-06-23 12:00:00  592.63  592.63  1.5  581.13  527.816891  583.202

But when I try to give the value, it doesn't work..

 df.loc[(df['high']/df['low'])>5]['low']=df.loc[(df['high']/df['low'])>5]['avg']

Can you help me?

解决方案

pandas' dataframe is base on index, so what you need is just

df.loc[(df['high']/df['low'])>5,'low']=df.avg
df
Out[1331]: 
     open    high     low   close      volume     avg
0  586.76  594.00  585.54  589.94  478.176973     NaN
1  589.94  595.49  588.23  592.63  448.689485  585.54
2  592.63  592.63  588.23  581.13  527.816891  588.23
3  581.13  586.33  578.58  580.96  728.424757    1.50

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