从 dicts 的 dict 创建一个内部值列表

Question

I am trying to figure out the max and min values for an inner value of a dict of dict s.

The dict looks like this:

{'ALLEN PHILLIP K': {'bonus': 4175000,
                     'exercised_stock_options': 1729541,
                     'expenses': 13868},
 'BADUM JAMES P': {'bonus': 'NaN',
                   'exercised_stock_options': 257817,
                   'expenses': 3486},
 ...
}

I want to figure out the minimum and maximum exercised_stock_options across all dictionaries.

I tried using pandas to do this, but couldn't find a way to shape the data appropriately. Then, I tried a simple for-loop in Python. My code for the for-loop doesn't work, and I can't figure out why (the dict of dicts is called data_dict ):

stock_options=[]
for person in range(len(data_dict)):
    stock_options.append(data_dict[person]['exercised_stock_options'])
print stock_options

Then I was going to take the max and min values of the list.

Any idea why this code doesn't work? Any alternative methods for figuring out the max and min of an inner value of a dict of dicts?

Answer 1

Here's a method that uses a list comprehension to get the exercised_stock_options from each dictionary and then prints out the minimum and maximum value from the data. Ignore the sample data, and you can modify it to suit your needs.

d = {'John Smith':{'exercised_stock_options':99},
     'Roger Park':{'exercised_stock_options':50},
     'Tim Rogers':{'exercised_stock_options':10}}
data = [d[person]['exercised_stock_options'] for person in d]
print min(data), max(data)

Answer 2

You are using range to get an index number for your main dictionary. What you really should do is get the keys for the dictionary and not the index. That is, person is the name of each one. Thus when person == 'ALLEN PHILLIP K' datadict[person] now gets the dictionary for that key.

Note that the Use items() to iterate across dictionary says that it is better to use d, v = data_dict.items() rather than looping over the dictionary itself. Also note the difference between Python 2 and Python 3.

people=[]
stock_options=[]
for person, stock_data in data_dict.items():
    people.append(person)
    stock_options.append(stock_data['exercised_stock_options'])
    # This lets you keep track of the people as well for future use
print stock_options
mymin = min(stock_options)
mymax = max(stock_options)
# process min and max values.

Best-practice

Use items() to iterate across dictionary

The updated code below demonstrates the Pythonic style for iterating through a dictionary. When you define two variables in a for loop in conjunction with a call to items() on a dictionary, Python automatically assigns the first variable as the name of a key in that dictionary, and the second variable as the corresponding value for that key.

 d = {"first_name": "Alfred", "last_name":"Hitchcock"} for key,val in d.items(): print("{} = {}".format(key, val))

Difference Python 2 and Python 3

In python 2.x the above examples using items would return a list with tuples containing the copied key-value pairs of the dictionary. In order to not copy and with that load the whole dictionary's keys and values inside a list to the memory you should prefer the iteritems method which simply returns an iterator instead of a list. In Python 3.x the iteritems is removed and the items method returns view objects. The benefit of these view objects compared to the tuples containing copies is that every change made to the dictionary is reflected in the view objects.

Answer 3

You need to iterate your dictionary .values() and return the value of "exercised_stock_options". You can use a simple list comprehensions to retrieve those values

>>> values = [value['exercised_stock_options'] for value in d.values()]
>>> values
[257817, 1729541]
>>> min(values)
257817
>>> max(values)
1729541

Answer 4

I've released lifter a few weeks ago exactly for these kind of tasks, I think you may find it useful. The only problem here is that you have a mapping (a dict of dicts) instead of a regular iterable.

Here is an answer using lifter:

from lifter.models import Model

# We create a model representing our data
Person = Model('Person')

# We convert your data to a regular iterable
iterable = []
for name, data in your_data.items():
    data['name'] = name
    iterable.append(data)

# we load this into lifter
manager = Person.load(iterable)

# We query the data
results = manager.aggregate(
    (Person.exercised_stock_options, min),
    (Person.exercised_stock_options, max),
)

You can of course achieve the same result using list comprehensions, however, it's sometimes handy to use a dedicated library, especially if you want to filter data using complex queries before fetching your results. For example, you could get your min and max value only for people with less than 10000 expenses:

# We filter the data
queryset = manager.filter(Person.expenses < 10000)

# we apply our aggregate on the filtered queryset
results = queryset.aggregate(
    (Person.exercised_stock_options, min),
    (Person.exercised_stock_options, max),
)