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Kyiv I have time series data in the following format, where one value represents the cumulative amount since the last recording. What I want to do is to "scatter" the accumulators that contain NaNs in the past so that this input: s = pd.Series([0, 0, np.nan, n
Finfino I have a list of objects. Each of these objects has a Nameproperty, and ObservablePairCollectionthis is just a custom dictionary that functions exactly like a dictionary, with key/value pairs. Given two strings, one for the name and one for the key, I
Rays In my class I have a private C# dictionary where the key is a string and the value is an object with PropertyChangedevents . Defined as follows: private readonly Dictionary<string, IAlsVariable> m_operatorAudibleSevServerVariables = new Dictionary<string,
Jaycechan The problem is to write a Python function that returns a list of keys in aDict with a value target. All keys and values in the dictionary are integers, and the keys in the list we return must be in ascending order. Here is what I have so far: def key
direct current I need to look up an order in a dictionary using the name, but can't figure out how to use the name (a string) to find a matching order since the value of the dictionary is the object order. Public Class MainForm
Public Shared orders As New
Chico Black So, I currently have this code, potions ={"small_health":15, "small_instant_exp":250}
selection = input("Pick one")
How would I make the selection have the same value as the key selected by the user if they have the same name? Nader Alec Hill
quitford Suppose we have a list of 2 dictionaries L1 and L2. I want a list of dictionaries that are in L2 but not in L1. In my case, I have L1 that is a subset of L2, so I'm not sure if this fact can be used to make any optimizations. figure point solution [_d
Kyiv I have time series data in the following format, where one value represents the cumulative amount since the last recording. What I want to do is to "scatter" the accumulators that contain NaNs in the past so that this input: s = pd.Series([0, 0, np.nan, n
Kyiv I have time series data in the following format, where one value represents the cumulative amount since the last recording. What I want to do is to "scatter" the accumulators that contain NaNs in the past so that this input: s = pd.Series([0, 0, np.nan, n
Kyiv I have time series data in the following format, where one value represents the cumulative amount since the last recording. What I want to do is to "scatter" the accumulators that contain NaNs in the past so that this input: s = pd.Series([0, 0, np.nan, n
Finfino I have a list of objects. Each of these objects has a Nameproperty, and ObservablePairCollectionthis is just a custom dictionary that functions exactly like a dictionary, with key/value pairs. Given two strings, one for the name and one for the key, I
Cassina my dictionary: d = {'a':1, 'b':2, 'c':3}
and my list of keys: keys = np.array(['a','b','a','c','a','b'])
I want to get a list of corresponding values without using a for loop I tried a for loop in the following way, but it is computationally too expe
ntf I'm writing a script to open and search the macOS Dictionary app In terminal, I can do this open dict://cheeseburger The app will then open the "Cheeseburger" entry Using python's subprocess module, I can do this: subprocess.Popen(["path_to_dictionary_app"
ntf I'm writing a script to open and search the macOS Dictionary app In terminal, I can do this open dict://cheeseburger The app will then open the "Cheeseburger" entry Using python's subprocess module, I can do this: subprocess.Popen(["path_to_dictionary_app"
Finfino I have a list of objects. Each of these objects has a Nameproperty, and ObservablePairCollectionthis is just a custom dictionary that functions exactly like a dictionary, with key/value pairs. Given two strings, one for the name and one for the key, I
Ben If I've been doing proper research on this, I've had some help before and a user said it would be nice to use a Dictionaryto store my Countrysum Places. So I created Dictionary: Dictionary<string, NewCountryClass> NTCD = new Dictionary<string, NewcountryCl
Angette Calaney MENU_PROMPT = "\nEnter 'a' to add a movie, 'l' to see your movies, 'f' to find a movie by title, or 'q' to quit: "
movies = []
# And another function here for the user menu
selection = input(MENU_PROMPT)
def movie_data():
title = input("E
Saranya Gupta I should find out the frequency of distinct keys in a list of dictionaries. E.g: Enter a list of dictionaries: [{'p1': 'val1', 'p2': 'val2', 'p3': 'val3', 'p4': 'val4'},
{'p1': 'val5', 'p7': 'val6', 'p3': 'val7'},
{'p1': 'val8', 'p2': 'val9', '
mimipc I want to be able to get all words in a dictionary (text file) that match a very simple constraint. Here are some examples of what I'm trying to achieve: For the string "abcd", find all words starting with "a" and containing "b", "c" and "d" at least on
Sonomad Is there an idiomatic (maybe more Pythonic) way to handle this: title = "my title"
name = "my name"
# [... on and on ...]
my_dict = {
'title': title,
'name': name,
# and on and on
}
Basically, the variable name (by encoding) is the key an
User 15278135 So I have a dictionary of lists of dictionaries (lst) and I am trying to loop through it, compare the values, and return the appropriate value. I have the following code to retrieve 2 arguments given from the command line, compare them by diction
pythonic metaphor I have a series of pandas integers (they are limited to some small finite subset) and a dictionary that doubles these possible integers. I want to create a new series that looks like dictionary[series]. What is the pandas idiomatic way? Alex
Finfino I have a list of objects. Each of these objects has a Nameproperty, and ObservablePairCollectionthis is just a custom dictionary that functions exactly like a dictionary, with key/value pairs. Given two strings, one for the name and one for the key, I
Kyiv I have time series data in the following format, where one value represents the cumulative amount since the last recording. What I want to do is to "scatter" the accumulators that contain NaNs in the past so that this input: s = pd.Series([0, 0, np.nan, n
Cassina my dictionary: d = {'a':1, 'b':2, 'c':3}
and my list of keys: keys = np.array(['a','b','a','c','a','b'])
I want to get a list of corresponding values without using a for loop I tried a for loop in the following way, but it is computationally too expe
Cassina my dictionary: d = {'a':1, 'b':2, 'c':3}
and my list of keys: keys = np.array(['a','b','a','c','a','b'])
I want to get a list of corresponding values without using a for loop I tried a for loop in the following way, but it is computationally too expe
Kyiv I have time series data in the following format, where one value represents the cumulative amount since the last recording. What I want to do is to "scatter" the accumulators that contain NaNs in the past so that this input: s = pd.Series([0, 0, np.nan, n
tool maker steve Over the years, I've needed code that does the following many times: Find a value in the dictionary; if it doesn't exist, add it to the dictionary (and return the new value). E.g: // Only one per account, so loading can be efficiently mana
Sonomad Is there an idiomatic (maybe more Pythonic) way to handle this: title = "my title"
name = "my name"
# [... on and on ...]
my_dict = {
'title': title,
'name': name,
# and on and on
}
Basically, the variable name (by encoding) is the key an