python – Pandas：枚举索引中的重复项

2019年8月4日 299次阅读

假设我有一个在不同键上发生的事件列表.

data = [
    {"key": "A", "event": "created"},
    {"key": "A", "event": "updated"},
    {"key": "A", "event": "updated"},
    {"key": "A", "event": "updated"},
    {"key": "B", "event": "created"},
    {"key": "B", "event": "updated"},
    {"key": "B", "event": "updated"},
    {"key": "C", "event": "created"},
    {"key": "C", "event": "updated"},
    {"key": "C", "event": "updated"},
    {"key": "C", "event": "updated"},
    {"key": "C", "event": "updated"},
    {"key": "C", "event": "updated"},
]

df = pandas.DataFrame(data)

我想首先在键上索引我的DataFrame,然后是枚举.它看起来像一个简单的unstack操作,但我无法找到如何正确地执行它.

我能做的最好的是

df.set_index("key", append=True).swaplevel(0, 1)

          event
key            
A   0   created
    1   updated
    2   updated
    3   updated
B   4   created
    5   updated
    6   updated
C   7   created
    8   updated
    9   updated
    10  updated
    11  updated
    12  updated

但我期待的是

          event
key            
A   0   created
    1   updated
    2   updated
    3   updated
B   0   created
    1   updated
    2   updated
C   0   created
    1   updated
    2   updated
    3   updated
    4   updated
    5   updated

我也尝试了类似的东西

df.groupby("key")["key"].count().apply(range).apply(pandas.Series).stack()

但订单未保留,因此我无法将结果应用为索引.此外,我觉得看起来非常标准的操作有点过分了……

任何的想法？

最佳答案 groupby cumcount

以下是几种方法：

# new version thanks @ScottBoston
df = df.set_index(['key', df.groupby('key').cumcount()])\
       .rename_axis(['key','count'])

# original version
df = df.assign(count=df.groupby('key').cumcount())\
       .set_index(['key', 'count'])

print(df)

             event
key count         
A   0      created
    1      updated
    2      updated
    3      updated
B   0      created
    1      updated
    2      updated
C   0      created
    1      updated
    2      updated
    3      updated
    4      updated
    5      updated
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