Can Pandas Read Excel's Group Structure into a MultIndex?

后端 未结 3 1054
栀梦
栀梦 2021-01-15 01:10

I have an Excel file with some (mostly) nicely grouped rows. I built a fake example below.

Is there a way to get read_excel in Pandas to produce a multiindex preser

3条回答
  •  轻奢々
    轻奢々 (楼主)
    2021-01-15 01:57

    No, pandas can't read such a structure.

    An alternative solution is to use pandas to read your data, but transform this into an easily accessible dictionary, rather than keeping your data in a dataframe with MultiIndex.

    There are 2 sensible requirements to make your data more usable:

    1. Make your investment fund names unique. This is trivial.
    2. Convert your Excel grouping to an additional column which indicates the parent of the row.

    In the below example, these 2 requirements are assumed.

    Setup

    from collections import defaultdict
    from functools import reduce
    import operator
    import pandas as pd
    
    df = pd.DataFrame({'name': ['Simpson Family', 'Marge Simpson', 'Maggies College Fund',
                                'MCF Investment 2', 'MS Investment 1', 'MS Investment 2', 'MS Investment 3',
                                'Homer Simpson', 'HS Investment 1', 'HS Investment 3', 'HS Investment 2',
                                'Griffin Family', 'Lois Griffin', 'LG Investment 2', 'LG Investment 3',
                                'Brian Giffin', 'BG Investment 3'],
                       'Value': [600, 450, 100, 100, 100, 200, 50, 150, 100, 50, 0, 200, 150, 100, 50, 50, 50],
                       'parent': ['Families', 'Simpson Family', 'Marge Simpson', 'Maggies College Fund',
                                  'Marge Simpson', 'Marge Simpson', 'Marge Simpson', 'Simpson Family',
                                  'Homer Simpson', 'Homer Simpson', 'Homer Simpson', 'Families',
                                  'Griffin Family', 'Lois Griffin', 'Lois Griffin', 'Griffin Family',
                                  'Brian Giffin']})
    
        Value                  name                parent  
    0     600        Simpson Family              Families   
    1     450         Marge Simpson        Simpson Family   
    2     100  Maggies College Fund         Marge Simpson   
    3     100      MCF Investment 2  Maggies College Fund   
    4     100       MS Investment 1         Marge Simpson   
    5     200       MS Investment 2         Marge Simpson   
    6      50       MS Investment 3         Marge Simpson   
    7     150         Homer Simpson        Simpson Family   
    8     100       HS Investment 1         Homer Simpson   
    9      50       HS Investment 3         Homer Simpson   
    10      0       HS Investment 2         Homer Simpson   
    11    200        Griffin Family              Families   
    12    150          Lois Griffin        Griffin Family   
    13    100       LG Investment 2          Lois Griffin   
    14     50       LG Investment 3          Lois Griffin   
    15     50          Brian Giffin        Griffin Family   
    16     50       BG Investment 3          Brian Giffin
    

    Step 1

    Define a child -> parent dictionary and some utility functions:

    child_parent_dict = df.set_index('name')['parent'].to_dict()
    
    tree = lambda: defaultdict(tree)
    
    d = tree()
    
    def get_all_parents(child):
    
        """Get all parents from hierarchy structure"""
    
        while child != 'Families':
            child = child_parent_dict[child]
            if child != 'Families':
                yield child
    
    def getFromDict(dataDict, mapList):
    
        """Iterate nested dictionary"""
    
        return reduce(operator.getitem, mapList, dataDict)
    
    def default_to_regular_dict(d):
    
        """Convert nested defaultdict to regular dict of dicts."""
    
        if isinstance(d, defaultdict):
            d = {k: default_to_regular_dict(v) for k, v in d.items()}
        return d
    

    Step 2

    Apply this to your dataframe. Use it to create a nested dictionary structure which will be more efficient for repeated queries.

    df['structure'] = df['name'].apply(lambda x: ['Families'] + list(get_all_parents(x))[::-1])
    
    for idx, row in df.iterrows():
        getFromDict(d, row['structure'])[row['name']]['Value'] = row['Value']
    
    res = default_to_regular_dict(d)
    

    Result

    Dataframe

        Value                  name                parent  \
    0     600        Simpson Family              Families   
    1     450         Marge Simpson        Simpson Family   
    2     100  Maggies College Fund         Marge Simpson   
    3     100      MCF Investment 2  Maggies College Fund   
    4     100       MS Investment 1         Marge Simpson   
    5     200       MS Investment 2         Marge Simpson   
    6      50       MS Investment 3         Marge Simpson   
    7     150         Homer Simpson        Simpson Family   
    8     100       HS Investment 1         Homer Simpson   
    9      50       HS Investment 3         Homer Simpson   
    10      0       HS Investment 2         Homer Simpson   
    11    200        Griffin Family              Families   
    12    150          Lois Griffin        Griffin Family   
    13    100       LG Investment 2          Lois Griffin   
    14     50       LG Investment 3          Lois Griffin   
    15     50          Brian Giffin        Griffin Family   
    16     50       BG Investment 3          Brian Giffin   
    
                                                structure  
    0                                          [Families]  
    1                          [Families, Simpson Family]  
    2           [Families, Simpson Family, Marge Simpson]  
    3   [Families, Simpson Family, Marge Simpson, Magg...  
    4           [Families, Simpson Family, Marge Simpson]  
    5           [Families, Simpson Family, Marge Simpson]  
    6           [Families, Simpson Family, Marge Simpson]  
    7                          [Families, Simpson Family]  
    8           [Families, Simpson Family, Homer Simpson]  
    9           [Families, Simpson Family, Homer Simpson]  
    10          [Families, Simpson Family, Homer Simpson]  
    11                                         [Families]  
    12                         [Families, Griffin Family]  
    13           [Families, Griffin Family, Lois Griffin]  
    14           [Families, Griffin Family, Lois Griffin]  
    15                         [Families, Griffin Family]  
    16           [Families, Griffin Family, Brian Giffin]
    

    Dictionary

    {'Families': {'Griffin Family': {'Brian Giffin': {'BG Investment 3': {'Value': 50},
                                                      'Value': 50},
                                     'Lois Griffin': {'LG Investment 2': {'Value': 100}, 'LG Investment 3': {'Value': 50},
                                                      'Value': 150},
                                     'Value': 200},
                  'Simpson Family': {'Homer Simpson': {'HS Investment 1': {'Value': 100}, 'HS Investment 2': {'Value': 0}, 'HS Investment 3': {'Value': 50},
                                                       'Value': 150},
                                     'Marge Simpson': {'MS Investment 1': {'Value': 100}, 'MS Investment 2': {'Value': 200}, 'MS Investment 3': {'Value': 50},
                                                       'Maggies College Fund': {'MCF Investment 2': {'Value': 100},
                                                                                'Value': 100},
                                                       'Value': 450},
                  'Value': 600}}}
    

提交回复
热议问题