如何从具有部分指定元组键的字典中的多列索引中设置新列? [英] How to set new columns in a multi-column index from a dict with partially specified tuple keys?

问题描述

我有一个以下列方式初始化的 Pandas 数据框:

I have a pandas dataframe initialized in the following way:

import pandas as pd

my_multi_index = pd.MultiIndex.from_tuples([('a', 'a1'), ('a', 'a2'), 
                                            ('b', 'b1'), ('b', 'b2')],
                                           names=['key1', 'key2']) 
df = pd.DataFrame(data=[[1, 2], [3, 4], [5, 6], [7, 8]],
                  columns=['col1', 'col2'],
                  index=my_multi_index)
print(df)

给出:

#            col1  col2
# key1 key2            
# a    a1       1     2
#      a2       3     4
# b    b1       5     6
#      b2       7     8

现在我想使用部分键切片向这个数据帧添加一个新列 desc1 但不是在代码中，我想从配置中做到这一点，即带有部分元组键的字典:

Now I'd like to add a new column desc1 to this dataframe using partial key slicing BUT not in code, I'd like to do this from configuration i.e. a dictionary with partial tuple keys:

# i'd like to externalize this and not hardcode it i.e. easier maintenance
df.loc[pd.IndexSlice['a', :], 'desc1'] = 'x'
df.loc[pd.IndexSlice['b', 'b1'], 'desc1'] = 'y1'
df.loc[pd.IndexSlice['b', 'b2'], 'desc1'] = 'y2'
print(df)

给出:

# key1 key2                  
# a    a1       1     2     x
#      a2       3     4     x
# b    b1       5     6    y1
#      b2       7     8    y2

请注意，设置 'x' 不依赖于 ('a', _) 键的第二个组件，而设置 'y1' 和 'y2' 确实依赖于 ('a', _) 键的第二个组件('b', 'b1') 键.一个可能的解决方案是完全指定映射，但如果我有一个 100 (a, _) 其分配不依赖于第二个组件，这也是不可取的.我希望达到上述结果，但不硬编码切片分配，而是希望从外部化字典中实现:

notice that setting 'x' doesn't depend on the second component of the ('a', _) key and setting 'y1' and 'y2' do depend on the second component of the ('b', 'b1') key. A possible solution is to fully specify the mapping but this is also not desirable if I have a 100 (a, _) whose assignment doesn't depend on the second component. I wish to reach the above result but not hard-coding the sliced assignments, instead I'd like to do it from an externalized dictionary:

我的配置字典如下所示:

My configuration dictionary would look like this:

my_dict = {
    ('a', None): 'x',
    ('b', 'b1'): 'y1',
    ('b', 'b2'): 'y2'
}

是否有一种 pythonic 和 pandas-tonic 的方式来应用带有部分指定键的字典来达到之前生成的切片分配?

Is there a pythonic and pandas-tonic way to apply this dictionary with partially specified keys to reach the sliced assignment produced before?

推荐答案

我们可以利用这样一个事实，即我们可以将元组作为 MultiIndex 切片器传递.此外，我们会稍微调整您的 my_dict.然后我们应用一个简单的 for 循环:

We can leverage the fact that we can pass tuples as a MultiIndex slicer. Also we slightly adjust your my_dict. Then we apply a simple for loop:

my_dict = {
    ('a',): 'x',
    ('b', 'b1'): 'y1',
    ('b', 'b2'): 'y2'
}

for idx, value in my_dict.items():
    df.loc[idx, 'desc1'] = value

           col1  col2 desc1
key1 key2                  
a    a1       1     2     x
     a2       3     4     x
b    b1       5     6    y1
     b2       7     8    y2

---

第二个选项是使用 Index.map 并填写你的字典中的第一个值，所以我们可以使用 Series.ffill:

---

Second option would be to use Index.map and filling in the first value in your dict, so we can use Series.ffill:

my_dict = {
    ('a', 'a1'): 'x',
    ('b', 'b1'): 'y1',
    ('b', 'b2'): 'y2'
}

df['desc1'] = df.index.map(my_dict)
df['desc1'] = df['desc1'].ffill()


           col1  col2 desc1
key1 key2                  
a    a1       1     2     x
     a2       3     4     x
b    b1       5     6    y1
     b2       7     8    y2

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