Found 35163 Articles for Programming

It is pretty easy to get the sum of values of a Python dictionary. You can first get the values in a list using the dict.values(). Then you can call the sum method to get the sum of these values. exampled = {    'foo': 10,    'bar': 20,    'baz': 30 } print(sum(d.values()))OutputThis will give the output −60

You can convert Python dictionary keys/values to lowercase by simply iterating over them and creating a new dict from the keys and values. For example, def lower_dict(d):    new_dict = dict((k.lower(), v.lower()) for k, v in d.items())    return new_dict a = {'Foo': "Hello", 'Bar': "World"} print(lower_dict(a))This will give the output{'foo': 'hello', 'bar': 'world'}If you want just the keys to be lower cased, you can call lower on just that. For example, def lower_dict(d):    new_dict = dict((k.lower(), v) for k, v in d.items())    return new_dict a = {'Foo': "Hello", 'Bar': "World"} print(lower_dict(a))This will give the output{'foo': 'Hello', 'bar': ... Read More

Python and javascript both have different representations for a dictionary. So you need an intermediate representation in order to pass data between them. The most commonly used intermediate representation is JSON, which is a simple lightweight data-interchange format.ExampleThe dumps function converts the dict to a string. For example, import json my_dict = {    'foo': 42,    'bar': {       'baz': "Hello",       'poo': 124.2    } } my_json = json.dumps(my_dict) print(my_json)OutputThis will give the output:'{"foo": 42, "bar": {"baz": "Hello", "poo": 124.2}}'ExampleThe load's function converts the string back to a dict. For example, import json my_str ... Read More

A python dictionary is a Hashmap. You can use the map data structure in C++ to mimic the behavior of a python dict. You can use map in C++ as follows:#include #include using namespace std; int main(void) {    /* Initializer_list constructor */    map m1 = {       {'a', 1},       {'b', 2},       {'c', 3},       {'d', 4},       {'e', 5}    };    cout

There are many C++/Python bindings. It boils down to what you use to communicate between C++ and python to read python dictionaries in c++. Most of these libraries(like Boost) handle the parsing themselves. You could use an intermediate data transfer format like JSON or XML to pass data between the 2 languages and then serialize and deserialize data using the respective libraries in these languages for these formats.

You can sort a list of dictionaries by values of the dictionary using the sorted function and passing it a lambda that tells which key to use for sorting. For example, A = [{'name':'john', 'age':45},      {'name':'andi', 'age':23},      {'name':'john', 'age':22},      {'name':'paul', 'age':35},      {'name':'john', 'age':21}] new_A = sorted(A, key=lambda x: x['age']) print(new_A)This will give the output:[{'name': 'john', 'age': 21}, {'name': 'john', 'age': 22}, {'name': 'andi', 'age': 23}, {'name': 'paul', 'age': 35}, {'name': 'john', 'age': 45}]You can also sort it in place using the sort function instead of the sorted function. For example, A ... Read More

If you have a dictionary of the following format:{    'KEY1':{'name':'foo', 'data':1351, 'completed':100},    'KEY2':{'name':'bar', 'data':1541, 'completed':12},    'KEY3':{'name':'baz', 'data':58413, 'completed':18} }And you want to sort by the key, completed within each entry, in a ascending order, you can use the sorted function with a lambda that specifies which key to use to sort the data. For example, my_collection = {    'KEY1':{'name':'foo', 'data':1351, 'completed':100},    'KEY2':{'name':'bar', 'data':1541, 'completed':12},    'KEY3':{'name':'baz', 'data':58413, 'completed':18} } sorted_keys = sorted(my_collection, key=lambda x: (my_collection[x]['completed'])) print(sorted_keys)This will give the output:['KEY2', 'KEY3', 'KEY1']Read More

Yes, a Python dictionary is thread safe. Actually, all built-ins in python are thread safe. You can read moreabout this in the documentation: https://docs.python.org/3/glossary.html#term-global-interpreter-lock

dicts in python are heavily optimized. Creating a dict from N keys or key/value pairs is O(N), fetching is O(1), putting is amortized O(1), and so forth. You don't need to optimize them explicitly. You can be sure of this as python under the hood implements its own classes using dicts.Don't compare lists/tuples to dicts/sets though as they solve different problems.

When using the old style of string formatting in python, ie, "" % (), if the thing after the percent is a tuple, python tries to break it down and pass individual items in it to the string. For example, tup = (1, 2, 3) print("this is a tuple %s" % (tup))This will give the output:TypeError: not all arguments converted during string formattingThis is because of the reason mentioned above. If you want to pass a tuple, you need to create a wrapping tuple using the (tup, ) syntax. For example, tup = (1, 2, 3) print("this is a tuple ... Read More