Python Pandas 滚动聚合一列列表

我有一个简单的数据框 df 和一列列表lists。我想根据以下内容生成一个附加列lists.

The df好像：

import pandas as pd
lists={1:[[1]],2:[[1,2,3]],3:[[2,9,7,9]],4:[[2,7,3,5]]}
#create test dataframe
df=pd.DataFrame.from_dict(lists,orient='index')
df=df.rename(columns={0:'lists'})
df

          lists
1           [1]
2     [1, 2, 3]
3  [2, 9, 7, 9]
4  [2, 7, 3, 5]

我想df看起来像这样：

df
Out[9]: 
          lists                 rolllists
1           [1]                       [1]
2     [1, 2, 3]              [1, 1, 2, 3]
3  [2, 9, 7, 9]     [1, 2, 3, 2, 9, 7, 9]
4  [2, 7, 3, 5]  [2, 9, 7, 9, 2, 7, 3, 5]

基本上我想“求和”/append滚动 2 列表。请注意第 1 行，因为我只有 1 个列表1 https://stackoverflow.com/questions/44501875/pandas-dataframe-column-of-lists-create-column-of-sets-of-cumulative-lists-an, rolllists 就是那个列表。但在第 2 行中，我有 2 个要附加的列表。然后对于第三行，附加df[2].lists and df[3].lists等等。我以前做过类似的事情，参考这个：Pandas Dataframe，列表列，创建累积列表集的列，并按记录差异进行记录 https://stackoverflow.com/questions/44501875/pandas-dataframe-column-of-lists-create-column-of-sets-of-cumulative-lists-an.
另外，如果我们可以得到上面的这一部分，那么我想在groupby（所以下面的例子是 1 组，例如df可能看起来像这样groupby):

  Group         lists                 rolllists
1     A           [1]                       [1]
2     A     [1, 2, 3]              [1, 1, 2, 3]
3     A  [2, 9, 7, 9]     [1, 2, 3, 2, 9, 7, 9]
4     A  [2, 7, 3, 5]  [2, 9, 7, 9, 2, 7, 3, 5]
5     B           [1]                       [1]
6     B     [1, 2, 3]              [1, 1, 2, 3]
7     B  [2, 9, 7, 9]     [1, 2, 3, 2, 9, 7, 9]
8     B  [2, 7, 3, 5]  [2, 9, 7, 9, 2, 7, 3, 5]

我尝试过各种方法，例如 df.lists.rolling(2).sum() ，但收到此错误：

TypeError: cannot handle this type -> object 

在 Pandas 0.24.1 中，不幸的是在 Pandas 0.22.0 中，该命令不会出错，而是返回与中完全相同的值lists。那么看起来新版本的 Pandas 无法对列表求和？这是次要问题。

喜欢任何帮助！玩得开心！

你可以从

import pandas as pd
mylists={1:[[1]],2:[[1,2,3]],3:[[2,9,7,9]],4:[[2,7,3,5]]}
mydf=pd.DataFrame.from_dict(mylists,orient='index')
mydf=mydf.rename(columns={0:'lists'})
mydf = pd.concat([mydf, mydf], axis=0, ignore_index=True)
mydf['group'] = ['A']*4 + ['B']*4

# initialize your new series
mydf['newseries'] = mydf['lists']

# define the function that appends lists overs rows
def append_row_lists(data):
    for i in data.index:
        try: data.loc[i+1, 'newseries'] = data.loc[i, 'lists'] + data.loc[i+1, 'lists']
        except: pass
    return data

# loop over your groups
for gp in mydf.group.unique():
    condition = mydf.group == gp
    mydf[condition] = append_row_lists(mydf[condition])

Output

          lists Group                 newseries
0           [1]     A                       [1]
1     [1, 2, 3]     A              [1, 1, 2, 3]
2  [2, 9, 7, 9]     A     [1, 2, 3, 2, 9, 7, 9]
3  [2, 7, 3, 5]     A  [2, 9, 7, 9, 2, 7, 3, 5]
4           [1]     B                       [1]
5     [1, 2, 3]     B              [1, 1, 2, 3]
6  [2, 9, 7, 9]     B     [1, 2, 3, 2, 9, 7, 9]
7  [2, 7, 3, 5]     B  [2, 9, 7, 9, 2, 7, 3, 5]