Convert pandas dictionary to a multi key dictionary where key order is irrelevant

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I would like to convert a pandas dataframe to a multi key dictionary, using 2 ore more columns as the dictionary key, and I would like these keys to be order irrelevant.

Here's an example of converting a pandas dictionary to a regular multi-key dictionary, where order is relevant.

import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randint(0,100,size=(5, 3)), columns=list('ABC'))

df_dict = df.set_index(['B', 'C']).to_dict()['A']
print(df_dict)

{(33, 21): 85, (61, 46): 88, (78, 12): 48, (89, 18): 65, (91, 19): 41}

so df_dict[(33, 21)] will get 85, but df_dict[(21, 33)] will result in a key error.

Potential Solutions

This is a SO question which covers ways to make order irrelevant dictionaries, using sorted, tuple, Counter, and/or frozenset.

Multiples-keys dictionary where key order doesn't matter

However, no apparent solutions jump out at me for using these datatypes and functions with Pandas conversion methods.

The next idea would be to convert the dictionary keys after the dataframe has been converted.

I tried this

new_d = {frozenset(key): value for key, value in df_dict}

But got this error

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-49-6a3244440ac2> in <module>()
----> 1 new_d = {frozenset(key): value for key, value in df_dict}
      2 new_d

<ipython-input-49-6a3244440ac2> in <dictcomp>(.0)
----> 1 new_d = {frozenset(key): value for key, value in df_dict}
      2 new_d

TypeError: 'int' object is not iterable
pandas dataframe dictionary python
2 Answers

Why not create from df

d = dict(zip(df[['B', 'C']].apply(frozenset,1),df['A']))
d
{frozenset({72, 12}): 34, frozenset({98, 76}): 82, frozenset({67, 7}): 35, frozenset({60, 70}): 18, frozenset({8, 53}): 81}
user_79645270

You're forgetting to loop over df_dict.items() instead of just df_dict ;)

>>> new_d = {frozenset(key): value for key, value in df_dict.items()}
>>> new_d
{frozenset({10, 99}): 92,
 frozenset({60, 76}): 54,
 frozenset({6, 20}): 31,
 frozenset({36, 46}): 31,
 frozenset({3, 68}): 59}

>>> new_d[frozenset({99, 10})]
92

Bonus: Since accessing everything using frozenset({...}) is gruesome, I wrote a little wrapper class to make it easier:

>>> class Test:
...     def __init__(self, fs):
...         self.fs = fs
...     def __getitem__(self, key):
...         return self.fs[frozenset(key)]
...     def __setitem__(self, key, val):
...         self.fs[frozenset(key)] = val
...     def __repr__(self):
...         import re
...         return re.sub(r'frozenset\({(.+?)}\)', r'(\1)', self.fs.__repr__())
...     __str__ = __repr__

>>> new_d = Test(new_d)
>>> new_d
{(10, 99): 92, (76, 60): 54, (20, 6): 31, (36, 46): 31, (3, 68): 59}

# Internally still just a dict of frozensets:
>>> new_d.fs
{frozenset({10, 99}): 92,
 frozenset({60, 76}): 54,
 frozenset({6, 20}): 31,
 frozenset({36, 46}): 31,
 frozenset({3, 68}): 59}

>>> new_d[10, 99]
92

>>> new_d[99, 10]
92

>>> new_d[99, 10] = 123456789

>>> new_d[10, 99]
123456789
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