如何使用pandas数据框创建滞后数据结构 [英] How to create a lagged data structure using pandas dataframe

问题描述

示例

s=pd.Series([5,4,3,2,1], index=[1,2,3,4,5])
print s 
1    5
2    4
3    3
4    2
5    1

是否有创建系列的有效方法.例如在每行中包含滞后值(在此示例中，直到滞后2)

Is there an efficient way to create a series. e.g. containing in each row the lagged values (in this example up to lag 2)

3    [3, 4, 5]
4    [2, 3, 4]
5    [1, 2, 3]

这对应于 s = pd.Series([[3,4,5]，[2,3,4]，[1,2,3]]，index = [3,4,5] )

对于时间序列很多且时间很长的数据帧，如何有效地做到这一点?

How can this be done in an efficient way for dataframes with a lot of timeseries which are very long?

谢谢

看到答案后进行了编辑

好的，最后我实现了这个功能:

ok, at the end I implemented this function:

def buildLaggedFeatures(s,lag=2,dropna=True):
'''
Builds a new DataFrame to facilitate regressing over all possible lagged features
'''
if type(s) is pd.DataFrame:
    new_dict={}
    for col_name in s:
        new_dict[col_name]=s[col_name]
        # create lagged Series
        for l in range(1,lag+1):
            new_dict['%s_lag%d' %(col_name,l)]=s[col_name].shift(l)
    res=pd.DataFrame(new_dict,index=s.index)

elif type(s) is pd.Series:
    the_range=range(lag+1)
    res=pd.concat([s.shift(i) for i in the_range],axis=1)
    res.columns=['lag_%d' %i for i in the_range]
else:
    print 'Only works for DataFrame or Series'
    return None
if dropna:
    return res.dropna()
else:
    return res 

它产生所需的输出并管理结果DataFrame中列的命名.

it produces the wished outputs and manages the naming of columns in the resulting DataFrame.

对于系列作为输入:

s=pd.Series([5,4,3,2,1], index=[1,2,3,4,5])
res=buildLaggedFeatures(s,lag=2,dropna=False)
   lag_0  lag_1  lag_2
1      5    NaN    NaN
2      4      5    NaN
3      3      4      5
4      2      3      4
5      1      2      3

，并以DataFrame作为输入:

and for a DataFrame as input:

s2=s=pd.DataFrame({'a':[5,4,3,2,1], 'b':[50,40,30,20,10]},index=[1,2,3,4,5])
res2=buildLaggedFeatures(s2,lag=2,dropna=True)

   a  a_lag1  a_lag2   b  b_lag1  b_lag2
3  3       4       5  30      40      50
4  2       3       4  20      30      40
5  1       2       3  10      20      30

推荐答案

如上所述，值得研究

As mentioned, it could be worth looking into the rolling_ functions, which will mean you won't have as many copies around.

一种解决方案是 concat 移位系列一起创建一个DataFrame:

One solution is to concat shifted Series together to make a DataFrame:

In [11]: pd.concat([s, s.shift(), s.shift(2)], axis=1)
Out[11]: 
   0   1   2
1  5 NaN NaN
2  4   5 NaN
3  3   4   5
4  2   3   4
5  1   2   3

In [12]: pd.concat([s, s.shift(), s.shift(2)], axis=1).dropna()
Out[12]: 
   0  1  2
3  3  4  5
4  2  3  4
5  1  2  3

这样做比列表上的工作更有效率...

这篇关于如何使用pandas数据框创建滞后数据结构的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！