Convert a string key to int in a Dictionary
Question:
My question is very similar to this one, except I have a dictionary of lists and I’m interested in changing both the key value and all elements in every list form string to int.
So for instance I’d like the dictionary:
{ '1':['1', '2', '3', '4'] , '2':['1', '4'] , '3':['43','176'] }
to become:
{ 1:[1, 2, 3, 4] , 2:[1, 4] , 3:[43,176] }
Is this possible?
More in general since I created this dictionary from a JSON format file
{"0": ["1", "2", "3", "4"], "1": ["0", "2", "3", "4", "27", "94",
"95", "97", "128", "217", "218", "317"], "2": ["0", "1", "3", "4",
"94", "95"], "3": ["0", "1", "2", "4", "377"], "4": ["0", "1", "2",
"3", "27", "28"], "5": ["6", "7", "8"], "6": ["5", "7", "8"], "7":
["5", "6", "8", "14", "23", "40", "74", "75", "76", "362", "371",
"372"], "8": ["5", "6", "7", "66"], "9": ["10", "11", "12"], "10":
["9", "11", "12", "56", "130", "131"]}
with the following instructions:
json_data = open("coauthorshipGraph.txt")
coautorshipDictionary = json.load( json_data )
json_data.close()
is there a way to do it directly at loading time?
Answers:
d = {'1':'145' , '2':'254' , '3':'43'}
d = {int(k):int(v) for k,v in d.items()}
>>> d
{1: 145, 2: 254, 3: 43}
for lists in values
>>> d = { '1':['1', '2', '3', '4'] , '2':['1', '4'] , '3':['43','176'] }
>>> d = {int(k):[int(i) for i in v] for k,v in d.items()}
in your case:
coautorshipDictionary = {int(k):int(v) for k,v in json.load(json_data)}
or
coautorshipDictionary = {
int(k):[int(i) for i in v] for k,v in json.load(json_data)}
This solution will work for the case where you have an iterable as your value, as in the json you provided.
my_dict = {"0": ["1", "2", "3", "4"], "1": ["0", "2", "3", "4", "27", "94", "95", "97", "128", "217", "218", "317"], "2": ["0", "1", "3", "4", "94", "95"], "3": ["0", "1", "2", "4", "377"], "4": ["0", "1", "2", "3", "27", "28"], "5": ["6", "7", "8"], "6": ["5", "7", "8"], "7": ["5", "6", "8", "14", "23", "40", "74", "75", "76", "362", "371", "372"], "8": ["5", "6", "7", "66"], "9": ["10", "11", "12"], "10": ["9", "11", "12", "56", "130", "131"]}
output_dict = {}
for key, value in my_dict.iteritems():
output_dict[int(key)] = [int(item) for item in value]
output_dict
Output:
{0: [1, 2, 3, 4],
1: [0, 2, 3, 4, 27, 94, 95, 97, 128, 217, 218, 317],
2: [0, 1, 3, 4, 94, 95],
3: [0, 1, 2, 4, 377],
4: [0, 1, 2, 3, 27, 28],
5: [6, 7, 8],
6: [5, 7, 8],
7: [5, 6, 8, 14, 23, 40, 74, 75, 76, 362, 371, 372],
8: [5, 6, 7, 66],
9: [10, 11, 12],
10: [9, 11, 12, 56, 130, 131]}
For the second part of the question, you can use a dict comprehension in line as you read the file. It’s obfuscated as hell though.
with open('coauthorshipGraph.txt', 'r') as f:
json_data = { int(key) : [int(item) for item in value] for key, value in json.load(f).iteritems()}
json_data
This yields the same output as above.
Similar to Decency’s answer, but taking advantage of the object_hook argument:
coautorshipDictionary = json.load(json_data, object_hook=lambda d: {int(k): [int(i) for i in v] if isinstance(v, list) else v for k, v in d.items()}) # iteritems() for Python 2
The main advantage of this method is that, if you ever end up with any nested dicts, the loader will handle each nested dict on its own as it loads the data without you having to write code to walk through your result dict. You could also add in checks for cases where values in lists are not numeric strings or the lists themselves contain dicts as well, if your JSON structure gets more complicated, and if your data will only have lists as the values for your top-level dict you can remove the if isinstance(v, list) else v part.
dict = { 'a':'100', 'b':'200', 'c':'300', 'd':'four_hundred', 'e':'500' }
dict_parse = {k: int(v) if v.isnumeric() else v for k, v in dict.items()}
>>> dict_parse
{ 'a': 100, 'b': 200, 'c': 300, 'd':'four_hundred', 'e':500}
When having a Python object (dict) that contains values with alphabetical strings and numerical strings, we can map and check against their type with string.isnumeric(), when dealing with float numbers amend the if statement to replace decimal point – you can apply same principal to negative numbers:
dict = { 'a':'10.0', 'b':'20.12', 'c':'300.3', 'd':'four_hundred', 'e':'500' }
dict_parse = {k: float(v) if v.replace(".", "").isnumeric() else v for k, v in dict.items()}
>>> dict_parse
{ 'a': 10.0, 'b': 20.12, 'c': 300.3, 'd':'four_hundred', 'e':500}
My question is very similar to this one, except I have a dictionary of lists and I’m interested in changing both the key value and all elements in every list form string to int.
So for instance I’d like the dictionary:
{ '1':['1', '2', '3', '4'] , '2':['1', '4'] , '3':['43','176'] }
to become:
{ 1:[1, 2, 3, 4] , 2:[1, 4] , 3:[43,176] }
Is this possible?
More in general since I created this dictionary from a JSON format file
{"0": ["1", "2", "3", "4"], "1": ["0", "2", "3", "4", "27", "94",
"95", "97", "128", "217", "218", "317"], "2": ["0", "1", "3", "4",
"94", "95"], "3": ["0", "1", "2", "4", "377"], "4": ["0", "1", "2",
"3", "27", "28"], "5": ["6", "7", "8"], "6": ["5", "7", "8"], "7":
["5", "6", "8", "14", "23", "40", "74", "75", "76", "362", "371",
"372"], "8": ["5", "6", "7", "66"], "9": ["10", "11", "12"], "10":
["9", "11", "12", "56", "130", "131"]}
with the following instructions:
json_data = open("coauthorshipGraph.txt")
coautorshipDictionary = json.load( json_data )
json_data.close()
is there a way to do it directly at loading time?
d = {'1':'145' , '2':'254' , '3':'43'}
d = {int(k):int(v) for k,v in d.items()}
>>> d
{1: 145, 2: 254, 3: 43}
for lists in values
>>> d = { '1':['1', '2', '3', '4'] , '2':['1', '4'] , '3':['43','176'] }
>>> d = {int(k):[int(i) for i in v] for k,v in d.items()}
in your case:
coautorshipDictionary = {int(k):int(v) for k,v in json.load(json_data)}
or
coautorshipDictionary = {
int(k):[int(i) for i in v] for k,v in json.load(json_data)}
This solution will work for the case where you have an iterable as your value, as in the json you provided.
my_dict = {"0": ["1", "2", "3", "4"], "1": ["0", "2", "3", "4", "27", "94", "95", "97", "128", "217", "218", "317"], "2": ["0", "1", "3", "4", "94", "95"], "3": ["0", "1", "2", "4", "377"], "4": ["0", "1", "2", "3", "27", "28"], "5": ["6", "7", "8"], "6": ["5", "7", "8"], "7": ["5", "6", "8", "14", "23", "40", "74", "75", "76", "362", "371", "372"], "8": ["5", "6", "7", "66"], "9": ["10", "11", "12"], "10": ["9", "11", "12", "56", "130", "131"]}
output_dict = {}
for key, value in my_dict.iteritems():
output_dict[int(key)] = [int(item) for item in value]
output_dict
Output:
{0: [1, 2, 3, 4],
1: [0, 2, 3, 4, 27, 94, 95, 97, 128, 217, 218, 317],
2: [0, 1, 3, 4, 94, 95],
3: [0, 1, 2, 4, 377],
4: [0, 1, 2, 3, 27, 28],
5: [6, 7, 8],
6: [5, 7, 8],
7: [5, 6, 8, 14, 23, 40, 74, 75, 76, 362, 371, 372],
8: [5, 6, 7, 66],
9: [10, 11, 12],
10: [9, 11, 12, 56, 130, 131]}
For the second part of the question, you can use a dict comprehension in line as you read the file. It’s obfuscated as hell though.
with open('coauthorshipGraph.txt', 'r') as f:
json_data = { int(key) : [int(item) for item in value] for key, value in json.load(f).iteritems()}
json_data
This yields the same output as above.
Similar to Decency’s answer, but taking advantage of the object_hook argument:
coautorshipDictionary = json.load(json_data, object_hook=lambda d: {int(k): [int(i) for i in v] if isinstance(v, list) else v for k, v in d.items()}) # iteritems() for Python 2
The main advantage of this method is that, if you ever end up with any nested dicts, the loader will handle each nested dict on its own as it loads the data without you having to write code to walk through your result dict. You could also add in checks for cases where values in lists are not numeric strings or the lists themselves contain dicts as well, if your JSON structure gets more complicated, and if your data will only have lists as the values for your top-level dict you can remove the if isinstance(v, list) else v part.
dict = { 'a':'100', 'b':'200', 'c':'300', 'd':'four_hundred', 'e':'500' }
dict_parse = {k: int(v) if v.isnumeric() else v for k, v in dict.items()}
>>> dict_parse
{ 'a': 100, 'b': 200, 'c': 300, 'd':'four_hundred', 'e':500}
When having a Python object (dict) that contains values with alphabetical strings and numerical strings, we can map and check against their type with string.isnumeric(), when dealing with float numbers amend the if statement to replace decimal point – you can apply same principal to negative numbers:
dict = { 'a':'10.0', 'b':'20.12', 'c':'300.3', 'd':'four_hundred', 'e':'500' }
dict_parse = {k: float(v) if v.replace(".", "").isnumeric() else v for k, v in dict.items()}
>>> dict_parse
{ 'a': 10.0, 'b': 20.12, 'c': 300.3, 'd':'four_hundred', 'e':500}