Let’s say I have a class that have multiple subclasses in a recursive/tree hierarchy: class Animal: pass class Cat(Animal): pass class HouseCat(Cat): pass And I have a function, that can create instances of these classes based on some condition: from typing import Union def creator(condition) -> Union[Animal, Cat, HouseCat]: # for codition1 return Animal() # ..

#### Category : union

I need to UNION two datasets in the code workbook of Palantir Foundry and I’m not sure how to do that. I want to use Pyspark to do this. I’m new to Foundry, please help! Source: Python..

I am writing a script to add missing keys within a list of dictionaries and assign them a default value. I start by building a set of all the possible keys that appear in one or more dictionaries. I adapted a nice snippet of code for this but I’m having trouble fully wrapping my head ..

I am currently trying to understand how the LSH process works and while implementing I got to a point where I am not sure how to proceed and I also can’t find any solutions. Currently, I am using random hyperplanes to calculate the dot product with tfidf vectors of the words of documents that I ..

What’s the difference between .union and | for sets in python? >>> a = set([1, 2, 3, 4]) >>> b = set([3, 4, 5, 6]) >>> a|b {1, 2, 3, 4, 5, 6} >>> a.union(b) {1, 2, 3, 4, 5, 6} Source: Python-3x..

**i have written this code and i think its time complexity is O(n+m) as time depends on both the input , am i right can anyone explain me like am a dumb 5 years old. thanks if you have a better algorithm you can suggest me . ** ”’ class Solution : def getUnion(self,a,b,): p= ..

everyone. I’m a student who’s started to learn pyspark recently. I don’t have that good knowledge about SQL. So here’s my question. Let’s say I have two data frames: 1: product list (Distinct):df1={"aaa","bbb","ccc","ddd"} keywords that are being searched by customers:df2={"aaa","bbb","ccc","aaa","abb","ccc"} I want to find out how many times certain keywords in the product list have ..

I would like to make the "disjunct union" the rows of a pyspark.DataFrame. (https://en.wikipedia.org/wiki/Disjoint_union) Here is an example: Suppose a pyspark.DataFrame "df" with the following rows: df: row1: A, B, C, D, null, null row2: X,Y,C,A,B, null How can I return "df2", consisting of one row only, regardless of the number of rows in "df": ..

I want to create a similarity index between distributions of variables and I thought of using the volumetric Intersection over Union (IoU) of Gaussian KDE adjusted to the data density. I will put an example here. I have the following distributions: import numpy as np import matplotlib.pyplot as plt import scipy.stats as st x1 = ..

I am trying to understand this code: edit_two_set = set() edit_two_set = set.union(*[edit_two_set.union(edit_one_letter(w, allow_switches)) for w in one]) Here one is a set of strings. allow_switches is True. edit_one_letter takes in one word and makes either one character insertion, deletion or one switch of corresponding characters. I understand: [edit_two_set.union(edit_one_letter(w, allow_switches)) for w in one] is ..

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