在 Pandas 中将两个 MultiIndex 级别合并为一个
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Merge two MultiIndex levels into one in Pandas
提问by Anmol Singh
I have a Pandas data frame which is MultiIndexed. The second level contains a year ([2014,2015]) and the third contains the month number ([1, 2, .., 12]). I would like to merge these two into a single level like - [1/2014, 2/2014 ..., 6/2015]. How could this be done?
我有一个多索引的 Pandas 数据框。第二层包含年份 ([2014,2015])，第三层包含月份编号 ([1, 2, .., 12])。我想将这两个合并为一个级别，例如 - [1/2014, 2/2014 ..., 6/2015]。这怎么可能？
I'm new to Pandas. Searched a lot but could not find any similar question/solution.
我是Pandas的新手。搜索了很多，但找不到任何类似的问题/解决方案。
Edit: I found a way to avoid having to do this altogether with the answer to this question. I should have been creating my data frame that way. This seems to be the way to go for indexing by DateTime.
编辑：我找到了一种方法来避免在回答这个问题时完全这样做。我应该以这种方式创建我的数据框。这似乎是通过 DateTime 进行索引的方法。
回答by piRSquared
Consider the pd.MultiIndexand pd.DataFrame, muxand df
考虑pd.MultiIndex和pd.DataFrame，mux和df
mux = pd.MultiIndex.from_product([list('ab'), [2014, 2015], range(1, 3)])
df = pd.DataFrame(dict(A=1), mux)
print(df)
A
a 2014 1 1
2 1
2015 1 1
2 1
b 2014 1 1
2 1
2015 1 1
2 1
We want to reassign to the index a list if lists that represent the index we want.
如果列表代表我们想要的索引，我们想为索引重新分配一个列表。
I want the 1st level the same
df.index.get_level_values(0)I want the new 2nd level to be a string concatenation of the current 2nd and 3rd levels but reverse the order
df.index.map('{0[2]}/{0[1]}'.format)
我想要第 1 级相同
df.index.get_level_values(0)我希望新的第二级是当前第二级和第三级的字符串连接，但顺序相反
df.index.map('{0[2]}/{0[1]}'.format)
df.index = [df.index.get_level_values(0), df.index.map('{0[2]}/{0[1]}'.format)]
print(df)
A
a 1/2014 1
2/2014 1
1/2015 1
2/2015 1
b 1/2014 1
2/2014 1
1/2015 1
2/2015 1
