熊猫df到字典的值作为从df列聚合的python列表

Question

I have a pandas df containing 'features' for stocks, which looks like this:

I am now trying to create a dictionary with unique sector as key , and a python list of tickers for that unique sector as values , so I end up having something that looks like this:

{'consumer_discretionary': ['AAP',
  'AMZN',
  'AN',
  'AZO',
  'BBBY',
  'BBY',
  'BWA',
  'KMX',
  'CCL',
  'CBS',
  'CHTR',
  'CMG',

etc.

I could iterate over the pandas df rows to create the dictionary, but I prefer a more pythonic solution. Thus far, this code is a partial solution:

df.set_index('sector')['ticker'].to_dict()

Any feedback is appreciated.

UPDATE:

The solution by @wrwrwr

df.set_index('ticker').groupby('sector').groups

partially works, but it returns a pandas series as a the value, instead of a python list . Any ideas about how to transform the pandas series into a python list in the same line and w/o having to iterate the dictionary?

Answer 1

Wouldn't f.set_index('ticker').groupby('sector').groups be what you want?

For example:

f = DataFrame({
        'ticker': ('t1', 't2', 't3'),
        'sector': ('sa', 'sb', 'sb'),
        'name': ('n1', 'n2', 'n3')})

groups = f.set_index('ticker').groupby('sector').groups
# {'sa': Index(['t1']), 'sb': Index(['t2', 't3'])}

To ensure that they have the type you want:

{k: list(v) for k, v in f.set_index('ticker').groupby('sector').groups.items()}

or:

f.set_index('ticker').groupby('sector').apply(lambda g: list(g.index)).to_dict()