获取Pandas数据帧中所选值的行和列标签 [英] Get the row and column labels for selected values in a Pandas dataframe

问题描述

我想获得与数据帧中某些条件匹配的值的行和列标签。为了保持它的趣味性，我需要它使用分层（多）索引。例如：

I'd like to get the row and column labels for values matching some condition in a dataframe. Just to keep it interesting, I need it to work with a hierarchical (multi-)index. For example:

df = pd.DataFrame(np.arange(16).reshape(4, 4), columns=pd.MultiIndex.from_product((('a', 'b'), ('x', 'y'))))

    a       b    
    x   y   x   y
0   0   1   2   3
1   4   5   6   7
2   8   9  10  11
3  12  13  14  15

现在假设我想要元素的行和列标签

Now let's say I want the row and column labels of the elements where

df % 6 == 0

       a             b       
       x      y      x      y
0   True  False  False  False
1  False  False   True  False
2  False  False  False  False
3   True  False  False  False

我想得到

[(0, ('a', 'x')), (1, ('b', 'x')), (3, ('a', 'x'))]

请注意我想要一个一般解决方案，它不依赖于索引是单调的，或特定的选择在我的例子中。这个问题已被多次询问，但答案并未概括：

Please note I would like a general solution, that does not rely on the index being monotonic, or the particular selection in my example. This questions has been asked many times, but the answers do not generalize:

• pandas数据框中最大值的索引和列：依赖排序查找最大值


• Pandas数据帧：返回最大值的行和列：不一般化


• 在pandas数据框中检索NaN值的索引：不返回行标签


• 返回大熊猫数据框中出现最小/最大值的索引/索引列表：不一致


• Pandas：Get每个值的索引和列值：不一般化/使用迭代

• index and column for the max value in pandas dataframe: relies on sorting to find max
• Pandas dataframe: return row AND column of maximum value(s): does not generalize
• Retrieve indices of NaN values in a pandas dataframe: does not return row label
• Return list of indices/index where a min/max value occurs in a pandas dataframe: does not generalize
• Pandas: Get each value's index and columns values: does not generalize / uses iteration

Pandas真的这么难吗？

Is this really so hard in Pandas?

推荐答案

使用 np.where 获取True值的序数索引：

Use np.where to obtain the ordinal indices of the True values:

import numpy as np
import pandas as pd
df = pd.DataFrame(np.arange(16).reshape(4, 4), 
                  columns=pd.MultiIndex.from_product((('a', 'b'), ('x', 'y'))))

mask = (df % 6 == 0)
i, j = np.where(mask)
print(list(zip(df.index[i], df.columns[j])))

收益率

[(0, ('a', 'x')), (1, ('b', 'x')), (3, ('a', 'x'))]

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