以特定顺序在 pandas 数据框中移动行 [英] Shift rows in pandas dataframe in a specific order

问题描述

我有一个如下所示的 Pandas 数据框:

I have a pandas dataframe which looks like this:

df = pd.DataFrame({
     'job': ['football','football', 'football', 'basketball', 'basketball', 'basketball', 'hokey', 'hokey', 'hokey', 'football','football', 'football', 'basketball', 'basketball', 'basketball', 'hokey', 'hokey', 'hokey'],
     'team': [4.0,5.0,9.0,2.0,3.0,6.0,1.0,7.0,8.0, 4.0,5.0,9.0,2.0,3.0,6.0,1.0,7.0,8.0],
     'cluster': [0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1]
     })

每个cluster 包含 9 个团队.每个集群有 3 支球队，每种运动类型football、basketball 和 hokey.我想对每个集群应用一个移位函数，以便团队的顺序以非常特定的方式发生(我尝试用颜色突出显示):

Each cluster contains 9 teams. Each cluster has 3 teams of each type of sport football, basketball and hokey. I want to apply a shift-function to each cluster, so that the order of teams chance in a very specific way (I tried to highlight it with color):

如何为更大的数据帧执行此转换(以上述方式移动行)?

How can I do this transformation (shift rows in a way shown above) for a much larger dataframe?

推荐答案

让我们用 groupby + cumcount 来创建一个基于列的顺序计数器 cluster 和 job 然后使用 sort_values 对 cluster 和这个 counter 上的数据帧进行排序:

Let's do groupby + cumcount to create a sequential counter based on the columns cluster and job then use sort_values to sort the dataframe on cluster and this counter:

df['j'] = df.groupby(['cluster', 'job']).cumcount()
df = df.sort_values(['cluster', 'j'], ignore_index=True).drop('j', axis=1)

---

           job  team  cluster
0     football   4.0        0
1   basketball   2.0        0
2        hokey   1.0        0
3     football   5.0        0
4   basketball   3.0        0
5        hokey   7.0        0
6     football   9.0        0
7   basketball   6.0        0
8        hokey   8.0        0
9     football   4.0        1
10  basketball   2.0        1
11       hokey   1.0        1
12    football   5.0        1
13  basketball   3.0        1
14       hokey   7.0        1
15    football   9.0        1
16  basketball   6.0        1
17       hokey   8.0        1

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