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Updating list of dictionaries in python using map

So I have a list that looks like this:
users = [{'id': 11, 'name': 'First'}, {'id': 22, 'name': 'Second'}, {'id':33, 'name': 'Third'}]
What I want to do is to update a users name by passing id, creating new user, and replacing old one with new user.
I want to get list of updated users, like this:
updated_users = list(map(update, users))
If I could send id to update func as argument, what I want to do, would look something like this:
def update(id):
if user['id'] == id:
new_user = some_fun()
user = new_user
return user
How should my update function look like?

I don't know why you want to use map and I think it's a wrong approach because map isn't for this kind of things (you could make it work for sure but it wouldn't be the way to go)
You can do something like that:
users = [{'id': 11, 'name': 'First'}, {'id': 22, 'name': 'Second'}, {'id':33, 'name': 'Third'}]
def update(id, new_name):
for user in users:
if user["id"] == id:
user["name"] = new_name
return
users.append({'id':id,'name':new_name}) # if not exist add user
print(users)
update(11,"Last")
update(1, "New_First")
print(users)
Output:
[{'id': 11, 'name': 'First'}, {'id': 22, 'name': 'Second'}, {'id': 33, 'name': 'Third'}]
[{'id': 11, 'name': 'Last'}, {'id': 22, 'name': 'Second'}, {'id': 33, 'name': 'Third'}, {'id': 1, 'name': 'New_First'}]

Filter/group dictionary by nested value

Here‘s a simplified example of some data I have:
{"id": "1234565", "fields": {"name": "john", "email":"john#example.com", "country": "uk"}}
The wholeo nested dictionary is a bigger list of address data. The goal is to create pairs of people from the list with randomized partners where partners from the same country should be preferd. So my first real issue is to find a good way to group them by that country value.
I‘m sure there‘s a smarter way to do this than iterating through the dict and writing all records out to some new list/dict?

I think this is close to what you need:
result = {key:[i for i in value] for key, value in itertools.groupby(people, lambda item: item["fields"]["country"])}
What this does is use itertools.groupby to group all people in the people list by their specified country. The resulting dictionary has countries as keys, and the unpacked groupings (matching people) as values. Input is expected as a list of dictionaries like the one in your example:
people = [{"id": "1234565", "fields": {"name": "john", "email":"john#example.com", "country": "uk"}},
{"id": "654321", "fields": {"name": "sam", "email":"sam#example.com", "country": "uk"}}]
Sample output:
>>> print(result)
>>> {'uk': [{'fields': {'name': 'john', 'email': 'john#example.com', 'country': 'uk'}, 'id': '1234565'}, {'fields': {'name': 'sam', 'email': 'sam#example.com', 'country': 'uk'}, 'id': '654321'}]}
For a cleaner result, the looping construct can be tweaked so that only the ID of each person is included in the result dict:
result = {key:[i["id"] for i in value] for key, value in itertools.groupby(people, lambda item: item["fields"]["country"])}
>>> print(result)
>>> {'uk': ['1234565', '654321']}
EDIT: Sorry, I forgot about the sorting. Simply sort the list of people by country before putting it through groupby. It should now work properly:
sort = sorted(people, key=lambda item: item["fields"]["country"])

Here is another one that uses defaultdict:
import collections
def make_groups(nested_dicts, nested_key):
default = collections.defaultdict(list)
for nested_dict in nested_dicts:
for value in nested_dict.values():
try:
default[value[nested_key]].append(nested_dict)
except TypeError:
pass
return default
To test the results:
import random
COUNTRY = {'af', 'br', 'fr', 'mx', 'uk'}
people = [{'id': i, 'fields': {
'name': 'name'+str(i),
'email': str(i)+'#email',
'country': random.sample(COUNTRY, 1)[0]}}
for i in range(10)]
country_groups = make_groups(people, 'country')
for country, persons in country_groups.items():
print(country, persons)
Random output:
fr [{'id': 0, 'fields': {'name': 'name0', 'email': '0#email', 'country': 'fr'}}, {'id': 1, 'fields': {'name': 'name1', 'email': '1#email', 'country': 'fr'}}, {'id': 4, 'fields': {'name': 'name4', 'email': '4#email', 'country': 'fr'}}]
br [{'id': 2, 'fields': {'name': 'name2', 'email': '2#email', 'country': 'br'}}, {'id': 8, 'fields': {'name': 'name8', 'email': '8#email', 'country': 'br'}}]
uk [{'id': 3, 'fields': {'name': 'name3', 'email': '3#email', 'country': 'uk'}}, {'id': 7, 'fields': {'name': 'name7', 'email': '7#email', 'country': 'uk'}}]
af [{'id': 5, 'fields': {'name': 'name5', 'email': '5#email', 'country': 'af'}}, {'id': 9, 'fields': {'name': 'name9', 'email': '9#email', 'country': 'af'}}]
mx [{'id': 6, 'fields': {'name': 'name6', 'email': '6#email', 'country': 'mx'}}]

Updating a value in a dictionary inside a dictionary

If I have a list of contact dictionaries like this:
{'name': 'Rob', 'phoneNumbers': [{'phone': '123-3214', 'type': 'home'}, {'phone': '456-3216', 'type': 'work'}]}
how could I update this dictionary to remove the dashes from the phone numbers in a list of contact dictionaries pythonically?

You could just nest loops:
for contact_dict in list_of_dicts:
for phone_dict in contact_dict['phoneNumbers']:
phone_dict['phone'] = phone_dict['phone'].replace('-', '')
This alters the values in-place.
Or you could create a whole new copy of the structure, with the alterations made:
[dict(contact, phoneNumbers=[
dict(phone_dict, phone=phone_dict['phone'].replace('-', ''))
for phone_dict in contact['phoneNumbers']])
for contact in list_of_dicts]
This creates a semi-shallow copy; only the phoneNumbers key is explicitly copied, but any other mutable values are just referenced by the new dictionaries.
Demo:
>>> list_of_dicts = [{'name': 'Rob', 'phoneNumbers': [{'phone': '123-3214', 'type': 'home'}, {'phone': '456-3216', 'type': 'work'}]}]
>>> [dict(contact, phoneNumbers=[
... dict(phone_dict, phone=phone_dict['phone'].replace('-', ''))
... for phone_dict in contact['phoneNumbers']])
... for contact in list_of_dicts]
[{'phoneNumbers': [{'phone': '1233214', 'type': 'home'}, {'phone': '4563216', 'type': 'work'}], 'name': 'Rob'}]
>>> for contact_dict in list_of_dicts:
... for phone_dict in contact_dict['phoneNumbers']:
... phone_dict['phone'] = phone_dict['phone'].replace('-', '')
...
>>> list_of_dicts
[{'phoneNumbers': [{'phone': '1233214', 'type': 'home'}, {'phone': '4563216', 'type': 'work'}], 'name': 'Rob'}]

Just str.replace the -
d ={'name': "Rob", 'phoneNumbers': [{'phone': '123-3214', 'type': 'home'}, {'phone': '456-3216', 'type': 'work'}]}
for dct in d["phoneNumbers"]:
dct['phone'] = dct['phone'].replace("-","",1)
Which gives you:
{'phoneNumbers': [{'phone': '1233214', 'type': 'home'}, {'phone': '4563216', 'type': 'work'}], 'name': 'Rob'}

how to delete empty dict inside list of dictionary?

How can I remove empty dict from list of dict as,
{
"ages":[{"age":25,"job":"teacher"},
{},{},
{"age":35,"job":"clerk"}
]
}
I am beginner to python.
Thanks in advance.

Try this
In [50]: mydict = {
....: "ages":[{"age":25,"job":"teacher"},
....: {},{},
....: {"age":35,"job":"clerk"}
....: ]
....: }
In [51]: mydict = {"ages":[i for i in mydict["ages"] if i]}
In [52]: mydict
Out[52]: {'ages': [{'age': 25, 'job': 'teacher'}, {'age': 35, 'job': 'clerk'}]}
OR simply use filter
>>>mylist = [{'age': 25, 'job': 'teacher'}, {}, {}, {'age': 35, 'job': 'clerk'}]
>>>filter(None, mylist)
[{'age': 25, 'job': 'teacher'}, {'age': 35, 'job': 'clerk'}]
So in your dict, apply it as
{
"ages":filter(None, [{"age":25,"job":"teacher"},
{},{},
{"age":35,"job":"clerk"}
])
}

There's a even simpler and more intuitive way than filter, and it works in Python 2 and Python 3:
You can do a "truth value testing" on a dict to test if it's empty or not:
>>> foo = {}
>>> if foo:
... print(foo)
...
>>>
>>> bar = {'key':'value'}
>>> if bar:
... print(bar)
...
{'key':'value'}
Therefore you can iterate over mylist and test for empty dicts with an if-statement:
>>> mylist = [{'age': 25, 'job': 'teacher'}, {}, {}, {'age': 35, 'job': 'clerk'}]
>>> [i for i in mylist if i]
[{'age': 25, 'job': 'teacher'}, {'age': 35, 'job': 'clerk'}]

If you are using Python 3, simply do:
list(filter(None, your_list_name))
This removes all empty dicts from your list.

This while loop will keep looping while there's a {} in the list and remove each one until there's none left.
while {} in dictList:
dictList.remove({})

You may try the following function:
def trimmer(data):
if type(data) is dict:
new_data = {}
for key in data:
if data[key]:
new_data[key] = trimmer(data[key])
return new_data
elif type(data) is list:
new_data = []
for index in range(len(data)):
if data[index]:
new_data.append(trimmer(data[index]))
return new_data
else:
return data
This will trim
{
"ages":[{"age":25,"job":"teacher"},
{},{},
{"age":35,"job":"clerk"}
]
}
and even
{'ages': [
{'age': 25, 'job': 'teacher', 'empty_four': []},
[], {},
{'age': 35, 'job': 'clerk', 'empty_five': []}],
'empty_one': [], 'empty_two': '',
'empty_three': {}
}
to this:
{'ages': [{'age': 25, 'job': 'teacher'}, {'age': 35, 'job': 'clerk'}]}

You can also try this
mylist = [{'age': 25, 'job': 'teacher'}, {}, {}, {'age': 35, 'job': 'clerk'}]
mylist=[i for i in mylist if i]
print(mylist)
Output:
[{'age': 25, 'job': 'teacher'}, {'age': 35, 'job': 'clerk'}]

Extract multiple key:value pairs from one dict to a new dict

I have a list of dict what some data, and I would like to extract certain key:value pairs into a new list of dicts. I know one way that I could do this would be to use del i['unwantedKey'], however, I would rather not delete any data but instead create a new dict with the needed data.
The column order might change, so I need something to extract the two key:value pairs from the larger dict into a new dict.
Current Data Format
[{'Speciality': 'Math', 'Name': 'Matt', 'Location': 'Miami'},
{'Speciality': 'Science', 'Name': 'Ben', 'Location': 'Las Vegas'},
{'Speciality': 'Language Arts', 'Name': 'Sarah', 'Location': 'Washington DC'},
{'Speciality': 'Spanish', 'Name': 'Tom', 'Location': 'Denver'},
{'Speciality': 'Chemistry', 'Name': 'Jim', 'Location': 'Dallas'}]
Code to delete key:value from dict
import csv
data= []
for line in csv.DictReader(open('data.csv')):
data.append(line)
for i in data:
del i['Speciality']
print data
Desired Data Format without using del i['Speciality']
[{'Name': 'Matt', 'Location': 'Miami'},
{'Name': 'Ben', 'Location': 'Las Vegas'},
{'Name': 'Sarah', 'Location': 'Washington DC'},
{'Name': 'Tom', 'Location': 'Denver'},
{'Name': 'Jim', 'Location': 'Dallas'}]

If you want to give a positive list of keys to copy over into the new dictionaries:
import csv
with open('data.csv', 'rb') as csv_file:
data = list(csv.DictReader(csv_file))
keys = ['Name', 'Location']
new_data = [dict((k, d[k]) for k in keys) for d in data]
print new_data

suppose we have,
l1 = [{'Location': 'Miami', 'Name': 'Matt', 'Speciality': 'Math'},
{'Location': 'Las Vegas', 'Name': 'Ben', 'Speciality': 'Science'},
{'Location': 'Washington DC', 'Name': 'Sarah', 'Speciality': 'Language Arts'},
{'Location': 'Denver', 'Name': 'Tom', 'Speciality': 'Spanish'},
{'Location': 'Dallas', 'Name': 'Jim', 'Speciality': 'Chemistry'}]
to create a new list of dictionaries that do not contain the keys 'Speciality' we can do,
l2 = []
for oldd in l1:
newd = {}
for k,v in oldd.items():
if k != 'Speciality':
newd[k] = v
l2.append(newd)
and now l2 will be your desired output. In general you can exclude an arbitrary list of keys like so
exclude_keys = ['Speciality', 'Name']
l2 = []
for oldd in l1:
newd = {}
for k,v in oldd.items():
if k not in exclude_keys:
newd[k] = v
l2.append(newd)
the same can be done with an include_keys variable
include_keys = ['Name', 'Location']
l2 = []
for oldd in l1:
newd = {}
for k,v in oldd.items():
if k in include_keys:
newd[k] = v
l2.append(newd)

You can create a new list of dicts limited to the keys you want with one line of code (Python 2.6+):
NLoD=[{k:d[k] for k in ('Name', 'Location')} for d in LoD]
Try it:
>>> LoD=[{'Speciality': 'Math', 'Name': 'Matt', 'Location': 'Miami'},
{'Speciality': 'Science', 'Name': 'Ben', 'Location': 'Las Vegas'},
{'Speciality': 'Language Arts', 'Name': 'Sarah', 'Location': 'Washington DC'},
{'Speciality': 'Spanish', 'Name': 'Tom', 'Location': 'Denver'},
{'Speciality': 'Chemistry', 'Name': 'Jim', 'Location': 'Dallas'}]
>>> [{k:d[k] for k in ('Name', 'Location')} for d in LoD]
[{'Name': 'Matt', 'Location': 'Miami'}, {'Name': 'Ben', 'Location': 'Las Vegas'}, {'Name': 'Sarah', 'Location': 'Washington DC'}, {'Name': 'Tom', 'Location': 'Denver'}, {'Name': 'Jim', 'Location': 'Dallas'}]
Since you are using csv, you can limit the columns that you read in the first place to the desired columns so you do not need to delete the undesired data:
dc=('Name', 'Location')
with open(fn) as f:
reader=csv.DictReader(f)
LoD=[{k:row[k] for k in dc} for row in reader]

keys_lst = ['Name', 'Location']
new_data={key:val for key,val in event.items() if key in keys_lst}
print(new_data)