Question

我正在努力研究如何使用条件对多个列值进行分组：

以下是我的数据作为pandas数据帧的样子：

{"bun_tag": "punctuation"},{"bun_tag": "quotation marks"},{"bun_tag": "document heading"},{"bun_tag": "document structure"}{"bun_tag": "multiple inversion"},{"bun_tag": "overloaded compound"},{"bun_tag": "syntactic ambiguity"},{"bun_tag": "excessive syntactic distance"}{"bun_tag": "omission"},{"bun_tag": "referential distortion"}{"bun_tag": "tense"},{"bun_tag": "modality"},{"bun_tag": "nominalisation"},{"bun_tag": "directive infinitive"}{"bun_tag": "good"}

Array
(
    [bun_tag] => good
)

{"bun_tag": "garden path"},{"bun_tag": "overloaded compound"}{"bun_tag": "missing determiner"},{"bun_tag": "referential ambiguity"}{"bun_tag": "garden path"}

Array
(
    [bun_tag] => garden path
)

{"bun_tag": "overloaded compound"}

Array
(
    [bun_tag] => overloaded compound
)

{"bun_tag": "capitalisation"},{"bun_tag": "title of document section"}{"bun_tag": "syntactic ambiguity"},{"bun_tag": "excessive syntactic distance"}{"bun_tag": "selectional restriction"}

Array
(
    [bun_tag] => selectional restriction
)

{"bun_tag": "garden path"},{"bun_tag": "overloaded compound"},{"bun_tag": "syntactic ambiguity"}{"bun_tag": "relational ambiguity"},{"bun_tag": "containment relationship"}{"bun_tag": "punctuation"},{"bun_tag": "weak interruption"}{"bun_tag": "meaning unclear"},{"bun_tag": "domain terminology"}{"bun_tag": "homonymy"},{"bun_tag": "nominalisation"},{"bun_tag": "meaning unclear"},{"bun_tag": "domain terminology"},{"bun_tag": "referential ambiguity"}{"bun_tag": "document structure"}

Array
(
    [bun_tag] => document structure
)

{"bun_tag": "polysemy"},{"bun_tag": "agent / receiver"},{"bun_tag": "relational distortion"}

我的目标是找出按ID分组的日期之间的日/小时或分钟差异。

我的输出看起来应该更像这样（差异在小时）：

id      trigger     timestamp
1       started     2017-10-01 14:00:1
1       ended       2017-10-04 12:00:1
2       started     2017-10-02 10:00:1
1       started     2017-10-03 11:00:1
2       ended       2017-10-04 12:00:1    
2       started     2017-10-05 15:00:1
1       ended       2017-10-05 16:00:1
2       ended       2017-10-05 17:00:1

我尝试了很多选项，但我不能提供最有效的解决方案。

以下是我的代码：

首先，我尝试将数据拆分为“已开始”＃39;并且＆＃39;结束了＆＃39;：

id      trigger     timestamp           trigger     timestamp               diff
1       started     2017-10-01 14:00:1  ended       2017-10-04 12:00:1      70
1       started     2017-10-03 11:00:1  ended       2017-10-05 16:00:1      53
2       started     2017-10-02 10:00:1  ended       2017-10-04 12:00:1      26
2       started     2017-10-05 15:00:1  ended       2017-10-05 17:00:1      2

然后：

df['started'] = df.groupby(['id', 'timestamp'])['trigger'] == 'started'

df['ended'] = df.groupby(['id', 'timestamp'])['trigger'] == 'ended'

但它没有工作。或

df.groupby(['id', 'started', 'ended'], as_index=True).sum()

也没有直觉结果。

可以指出一些正确的方向如何用熊猫做到这一点？我还将在数据中使用空匹配，如何使用df['started'] = df.groupby(['trigger'])['timestamp'].np.where(df['trigger']=='started')将NaN或缺少的数据添加到新数据框中。

Answer 1

将id和trigger设为索引
由于索引包含重复的条目，因此使用groupwise cumcount附加另一个索引列。完全相同，df必须有一个MultiIndex，其中包含3列
unstack timestamp
按小时查找列之间的差异并将结果返回

df['timestamp'] = pd.to_datetime(df['timestamp']) # if necessary

i = df.groupby(['id', 'trigger']).cumcount()
df.set_index(['id', i, 'trigger']).timestamp.unstack().assign(
       diff=lambda d: d.ended.sub(d.started).dt.total_seconds() / 3600
)

感谢piRSquared的改进。

v

                  timestamp                      diff
trigger               ended             started      
id                                                   
1  0    2017-10-04 12:00:01 2017-10-01 14:00:01  70.0
   1    2017-10-05 16:00:01 2017-10-03 11:00:01  53.0
2  0    2017-10-04 12:00:01 2017-10-02 10:00:01  50.0
   1    2017-10-05 17:00:01 2017-10-05 15:00:01   2.0

结果与您的问题中描述的不完全相同，但我相信MultiIndex列可以更清晰地表示您的输出，而不是两个触发列。

Pandas - 具有来自另一列的条件的groupby列

1 个答案: