在 pandas 数据框中访问内部多索引级别的最后一个元素 [英] Access last elements of inner multiindex level in pandas dataframe

问题描述

在一个 multi index 熊猫数据框中，我想访问第二个索引的 last 元素，以获取第一个索引的所有值.第二个索引中的级别数取决于第一个索引的值.我浏览了 pandas多索引文档，但找不到任何能做的事情那个.

In a multi index pandas dataframe I want to access the last element of the second index for all values of the first index. The number of levels in the second index vary depending on the value of the first index. I went through the pandas multi index documentation but could not find anything that does that.

例如，对于以下数据框:

For example, for the data frame below:

arrays = [ ['bar', 'bar', 'baz', 'foo', 'foo', 'foo',   'qux'],
           ['one', 'two', 'one', 'one', 'two', 'three', 'one']]
tuples = list(zip(*arrays))
index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
df = pd.DataFrame(np.random.randn(7, 3), index=index, columns=['A', 'B', 'C'])
df
                 A         B         C
first second
bar   one     0.289163 -0.464633 -0.060487
      two     0.224442  0.177609  2.156436
baz   one    -0.262329 -0.248384  0.925580
foo   one     0.051350  0.452014  0.206809
      two     2.757255 -0.739196  0.183735
      three  -0.064909 -0.963130  1.364771
qux   one    -1.330857  1.881588 -0.262170

我想得到:

                 A         B         C
first second
bar   two     0.224442  0.177609  2.156436
baz   one    -0.262329 -0.248384  0.925580
foo   three  -0.064909 -0.963130  1.364771
qux   one    -1.330857  1.881588 -0.262170

我正在使用的 dataframes 有超过 10M 行，因此我想避免显式循环.

The dataframes I am working with have over 10M lines so I want to avoid explicit looping.

推荐答案

使用 groupby 和因为 last 失去了第二级:

print (df.groupby(level='first').last())         
              A         B         C
first                              
bar    0.053054 -0.555819  0.589998
baz   -0.868676  1.293633  1.339474
foo    0.407454  0.738872  1.811894
qux   -0.346014 -1.491270  0.446772

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