在 pandas 中将两个MultiIndex级别合并为一个级别 [英] Merge two MultiIndex levels into one in Pandas

问题描述

我有一个MultiIndexed的Pandas数据框.第二级包含年份([2014,2015])，第三级包含月份号([1、2，..，12]).我想将这两个合并成一个单一的级别，例如-[1/2014，2/2014 ...，6/2015].怎么办呢?

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?

我是熊猫的新手.搜索了很多，但找不到任何类似的问题/解决方案.

I'm new to Pandas. Searched a lot but could not find any similar question/solution.

我找到了一种避免完全与

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.

推荐答案

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

---

如果要表示我们想要的索引的列表，我们想将一个列表重新分配给索引.

---

We want to reassign to the index a list if lists that represent the index we want.

• 我希望第一级相同

• I want the 1st level the same

df.index.get_level_values(0)

我希望新的2级是当前2级和3级的字符串连接，但顺序相反

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)

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

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