如何正确使用包含间隔的多索引的Pandas Dataframe？

时间：2017-12-03 18:40:51

标签： python pandas dataframe

我试图切入具有由IntervalIndex和常规索引组成的MultiIndex的DataFrame。示例代码：

from pandas import Interval as ntv

df = pd.DataFrame.from_records([
   {'id': 1, 'var1': 0.1, 'ntv': ntv(0,10), 'E': 1}, 
   {'id':2, 'var1': 0.5, 'ntv': ntv(0,12), 'E': 0}
], index=('ntv', 'id'))

看起来像这样：

            E  var1
ntv     id
(0, 10] 1   1   0.1
(0, 12] 2   0   0.5

我想要做的是以特定值切入DataFrame并返回具有包含该值的间隔的所有行。例如：

df.loc[4]

应该返回（平凡）

    E  var1
id
1   1   0.1
2   0   0.5

问题是我不断获得关于索引的TypeError，而docs显示了类似的操作（但是在单级索引上）确实产生了我正在寻找的内容

TypeError: only integer scalar arrays can be converted to a scalar index

我尝试了很多东西，似乎没有什么能正常工作。我可以在数据框中包含id列，但我宁愿保持我的索引唯一，我会不断地调用set_index('id')。

我感觉要么a）我错过了关于MultiIndexes的东西，或者b）在MultiIndex中使用IntervalIndex存在错误/歧义。

5 个答案:

答案 0 :(得分：6)

由于我们正在发言间隔，因此有一个名为get_loc的方法来查找间隔之间具有值的行。说出我的意思：

from pandas import Interval as ntv

df = pd.DataFrame.from_records([
   {'id': 1, 'var1': 0.1, 'ntv': ntv(0,10), 'E': 1}, 
   {'id':2, 'var1': 0.5, 'ntv': ntv(0,12), 'E': 0}
], index=('ntv', 'id'))

df.iloc[(df.index.get_level_values(0).get_loc(4))]
            E  var1
ntv     id         
(0, 10] 1   1   0.1
(0, 12] 2   0   0.5

df.iloc[(df.index.get_level_values(0).get_loc(11))]
             E  var1
ntv     id         
(0, 12] 2   0   0.5

如果你有一行的多行数据，这也适用于

df = pd.DataFrame.from_records([
   {'id': 1, 'var1': 0.1, 'ntv': ntv(0,10), 'E': 1}, 
   {'id': 3, 'var1': 0.1, 'ntv': ntv(0,10), 'E': 1},
   {'id':2, 'var1': 0.5, 'ntv': ntv(0,12), 'E': 0}
], index=('ntv', 'id'))

df.iloc[(df.index.get_level_values(0).get_loc(4))]

            E  var1
ntv     id         
(0, 10] 1   1   0.1
        3   1   0.1
(0, 12] 2   0   0.5

如果你用列表理解来计算时间，这种方法对于大型数据帧来说更快，即

ndf = pd.concat([df]*10000)

%%timeit
ndf.iloc[ndf.index.get_level_values(0).get_loc(4)]
10 loops, best of 3: 32.8 ms per loop

%%timeit
intervals = ndf.index.get_level_values(0)
mask = [4 in i for i in intervals]
ndf.loc[mask]
1 loop, best of 3: 193 ms per loop

答案 1 :(得分：3)

所以我做了一些挖掘尝试去理解这个问题。如果我尝试运行您的代码，则会发生以下情况。 您尝试使用索引索引到索引标签 “slice（array（[0,1]，dtype = int64），array（[1,2]，dtype = int64），None）”

（当我说index_type我指的是Pandas数据类型）

index_type的标签是映射到index_type的levels数组的索引列表。以下是文档中的示例。 

+---------------------------------------------------+
 | id_no | fullname | gender | dec 1 | dec 2 | dec 3 |
 | 1     | stud1    | male   | A     | P     |  P    |

注意标签中的第二个列表如何连接到级别的顺序。 level [1] [1]等于红色，等级[1] [0]等于蓝色。

无论如何，这就是说我不相信intervalindex意味着以重叠的方式使用。如果你看一下它的原始提案 https://github.com/pandas-dev/pandas/issues/7640

“IntervalIndex将是一个单调且不重叠的一维间隔数组。”

我的建议是将间隔移到一列中。您可以用numba编写一个简单的函数来测试每个区间中是否有数字。你介意解释你从间隔中受益的方式吗？

答案 2 :(得分：2)

躲避@ Dark solution Index.get_loc，Index.get_indexer只需拨打idx = df.index.get_level_values(0) df.iloc[idx.get_indexer([4])] ，因此当你不打电话时，调用基础方法会更有效率。 ; t有其他参数和繁文缛节。

intervals = df.index.get_level_values(0)
mask = [4 in i for i in intervals]
df.loc[mask]

我最初提出的解决方案：

df.reset_index(level=1, drop=True).loc[4] # good
df.loc[4]  # TypeError

无论如何，它确实很奇怪，虽然它们会返回两个不同的结果，但看起来它与索引是唯一的/单调的/两者都不相符：

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2017-12-03T20:06:51.945393+00:00 app[web.1]: [cf3559e7-f176-4ddd-848e-ae08dede69ce] vendor/bundle/ruby/2.3.0/gems/actionpack-5.1.4/lib/action_dispatch/middleware/callbacks.rb:26:in `block in call'
2017-12-03T20:06:51.945392+00:00 app[web.1]: [cf3559e7-f176-4ddd-848e-ae08dede69ce] vendor/bundle/ruby/2.3.0/gems/actionpack-5.1.4/lib/action_dispatch/middleware/cookies.rb:613:in `call'
2017-12-03T20:06:51.945394+00:00 app[web.1]: [cf3559e7-f176-4ddd-848e-ae08dede69ce] vendor/bundle/ruby/2.3.0/gems/activesupport-5.1.4/lib/active_support/callbacks.rb:97:in `run_callbacks'
2017-12-03T20:06:51.945395+00:00 app[web.1]: [cf3559e7-f176-4ddd-848e-ae08dede69ce] vendor/bundle/ruby/2.3.0/gems/actionpack-5.1.4/lib/action_dispatch/middleware/callbacks.rb:24:in `call'
2017-12-03T20:06:51.945396+00:00 app[web.1]: [cf3559e7-f176-4ddd-848e-ae08dede69ce] vendor/bundle/ruby/2.3.0/gems/actionpack-5.1.4/lib/action_dispatch/middleware/show_exceptions.rb:31:in `call'
2017-12-03T20:06:51.945397+00:00 app[web.1]: [cf3559e7-f176-4ddd-848e-ae08dede69ce] vendor/bundle/ruby/2.3.0/gems/railties-5.1.4/lib/rails/rack/logger.rb:36:in `call_app'
2017-12-03T20:06:51.945395+00:00 app[web.1]: [cf3559e7-f176-4ddd-848e-ae08dede69ce] vendor/bundle/ruby/2.3.0/gems/actionpack-5.1.4/lib/action_dispatch/middleware/debug_exceptions.rb:59:in `call'
2017-12-03T20:06:51.945398+00:00 app[web.1]: [cf3559e7-f176-4ddd-848e-ae08dede69ce] vendor/bundle/ruby/2.3.0/gems/railties-5.1.4/lib/rails/rack/logger.rb:24:in `block in call'
2017-12-03T20:06:51.945398+00:00 app[web.1]: [cf3559e7-f176-4ddd-848e-ae08dede69ce] vendor/bundle/ruby/2.3.0/gems/activesupport-5.1.4/lib/active_support/tagged_logging.rb:69:in `block in tagged'
2017-12-03T20:06:51.945410+00:00 app[web.1]: [cf3559e7-f176-4ddd-848e-ae08dede69ce] vendor/bundle/ruby/2.3.0/gems/activesupport-5.1.4/lib/active_support/tagged_logging.rb:26:in `tagged'
2017-12-03T20:06:51.945411+00:00 app[web.1]: [cf3559e7-f176-4ddd-848e-ae08dede69ce] vendor/bundle/ruby/2.3.0/gems/activesupport-5.1.4/lib/active_support/tagged_logging.rb:69:in `tagged'
2017-12-03T20:06:51.945412+00:00 app[web.1]: [cf3559e7-f176-4ddd-848e-ae08dede69ce] vendor/bundle/ruby/2.3.0/gems/railties-5.1.4/lib/rails/rack/logger.rb:24:in `call'
2017-12-03T20:06:51.945412+00:00 app[web.1]: [cf3559e7-f176-4ddd-848e-ae08dede69ce] vendor/bundle/ruby/2.3.0/gems/actionpack-5.1.4/lib/action_dispatch/middleware/remote_ip.rb:79:in `call'
2017-12-03T20:06:51.945413+00:00 app[web.1]: [cf3559e7-f176-4ddd-848e-ae08dede69ce] vendor/bundle/ruby/2.3.0/gems/actionpack-5.1.4/lib/action_dispatch/middleware/request_id.rb:25:in `call'
2017-12-03T20:06:51.945414+00:00 app[web.1]: [cf3559e7-f176-4ddd-848e-ae08dede69ce] vendor/bundle/ruby/2.3.0/gems/rack-2.0.3/lib/rack/method_override.rb:22:in `call'
2017-12-03T20:06:51.945415+00:00 app[web.1]: [cf3559e7-f176-4ddd-848e-ae08dede69ce] vendor/bundle/ruby/2.3.0/gems/rack-2.0.3/lib/rack/runtime.rb:22:in `call'
2017-12-03T20:06:51.945417+00:00 app[web.1]: [cf3559e7-f176-4ddd-848e-ae08dede69ce] vendor/bundle/ruby/2.3.0/gems/activesupport-5.1.4/lib/active_support/cache/strategy/local_cache_middleware.rb:27:in `call'
2017-12-03T20:06:51.945417+00:00 app[web.1]: [cf3559e7-f176-4ddd-848e-ae08dede69ce] vendor/bundle/ruby/2.3.0/gems/actionpack-5.1.4/lib/action_dispatch/middleware/executor.rb:12:in `call'
2017-12-03T20:06:51.945418+00:00 app[web.1]: [cf3559e7-f176-4ddd-848e-ae08dede69ce] vendor/bundle/ruby/2.3.0/gems/actionpack-5.1.4/lib/action_dispatch/middleware/static.rb:125:in `call'
2017-12-03T20:06:51.945419+00:00 app[web.1]: [cf3559e7-f176-4ddd-848e-ae08dede69ce] vendor/bundle/ruby/2.3.0/gems/rack-2.0.3/lib/rack/sendfile.rb:111:in `call'
2017-12-03T20:06:51.945420+00:00 app[web.1]: [cf3559e7-f176-4ddd-848e-ae08dede69ce] vendor/bundle/ruby/2.3.0/gems/puma-3.11.0/lib/puma/configuration.rb:225:in `call'
2017-12-03T20:06:51.945420+00:00 app[web.1]: [cf3559e7-f176-4ddd-848e-ae08dede69ce] vendor/bundle/ruby/2.3.0/gems/railties-5.1.4/lib/rails/engine.rb:522:in `call'
2017-12-03T20:06:51.945422+00:00 app[web.1]: [cf3559e7-f176-4ddd-848e-ae08dede69ce] vendor/bundle/ruby/2.3.0/gems/puma-3.11.0/lib/puma/server.rb:438:in `process_client'
2017-12-03T20:06:51.945421+00:00 app[web.1]: [cf3559e7-f176-4ddd-848e-ae08dede69ce] vendor/bundle/ruby/2.3.0/gems/puma-3.11.0/lib/puma/server.rb:624:in `handle_request'
2017-12-03T20:06:51.945423+00:00 app[web.1]: [cf3559e7-f176-4ddd-848e-ae08dede69ce] vendor/bundle/ruby/2.3.0/gems/puma-3.11.0/lib/puma/server.rb:302:in `block in run'
2017-12-03T20:06:51.945424+00:00 app[web.1]: [cf3559e7-f176-4ddd-848e-ae08dede69ce] vendor/bundle/ruby/2.3.0/gems/puma-3.11.0/lib/puma/thread_pool.rb:120:in `block in spawn_thread'

答案 3 :(得分：2)

这不是一个真正的解决方案，我不完全理解，但认为它可能与你的间隔指数不单调（因为你有重叠的间隔）。我想在某种意义上可以认为是单调的，所以也许你可以说重叠意味着指数不是唯一的？

无论如何，看看这个github问题：

ENH: Implement MultiIndex.is_monotonic_decreasing #17455

以下是您的数据示例，但将间隔更改为非重叠（0,6）＆amp; （7,12）：

df = pd.DataFrame.from_records([
   {'id': 1, 'var1': 0.1, 'ntv': ntv(0, 6), 'E': 1}, 
   {'id': 2, 'var1': 0.5, 'ntv': ntv(7,12), 'E': 0}
], index=('ntv', 'id'))

现在，loc正常工作：

df.loc[4]

    E  var1
id         
1   1   0.1

答案 4 :(得分：0)

def check_value(num):
    return df[[num in i for i in map(lambda x: x[0], df.index)]] 

a = check_value(4)
a
>> 
            E  var1
ntv     id         
(0, 10] 1   1   0.1
(0, 12] 2   0   0.5

如果要删除索引级别，可以添加

a.index = a.droplevel(0)
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