本文介绍了将 Pandas groupby 对象转换为数据框列表的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
问题描述
假设我有以下数据框,并希望按 ys 分组:
xs ys0 0 01 1 02 2 13 3 1
我可以通过运行来做到这一点
grouped = df.groupby('ys')
我可以很好地遍历这个新的 groupby 对象,但是我想要在以下循环中由 group
访问的数据帧列表:
为名称,分组分组:做某事(群)
这可能吗?
解决方案
当然,只需遍历组即可!
>>>将熊猫导入为 pd,将 numpy 导入为 np>>>df = pd.DataFrame(dict(xs=list(range(4)), ys=[0,0,1,1]))>>>dfx ys0 0 01 1 02 2 13 3 1>>>grouped = df.groupby('ys')>>>数据帧 = [分组为 _,分组分组]>>>数据框[ x y y0 0 01 1 0, xs ys2 2 13 3 1]>>>Say I have the following dataframe, and want to group-by the ys:
xs ys
0 0 0
1 1 0
2 2 1
3 3 1
I can do this by running
grouped = df.groupby('ys')
I can iterate through this new groupby object fine, but instead I want a list of the dataframes that are accessed by group
in the following loop:
for name, group in grouped:
do_something(group)
Is this possible?
解决方案
Sure, just iterate over the groups!
>>> import pandas as pd, numpy as np
>>> df = pd.DataFrame(dict(xs=list(range(4)), ys=[0,0,1,1]))
>>> df
xs ys
0 0 0
1 1 0
2 2 1
3 3 1
>>> grouped = df.groupby('ys')
>>> dataframes = [group for _, group in grouped]
>>> dataframes
[ xs ys
0 0 0
1 1 0, xs ys
2 2 1
3 3 1]
>>>
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