arr = np.array(['aasdfads', 'asfdads', 'fdfsdfaf']) >>> [len(i) for i in arr] [8, 7, 8]
Here is what the above code is Doing:
1. Create a list of strings
2. Create a new list by applying the len() function to each element of the list
#To import multiple modules in one-line in Python, do the following import {module1 ex. pygame}, {module2 ex. random}, {module3 ex. math} ”’ the comma separates the modules which allows you to import multiple modules in one line ”’ Here is what the above code is Doing: 1. import the pygame module 2. import the random…
#in template you can use the filter length on your model post {{ posts|length }} Here is what the above code is Doing: 1. We’re using the | (pipe) character to apply a filter to a variable. 2. In this case, we’re using the length filter, which returns the number of items in a list….
import logging logging.basicConfig( format=’%(asctime)s %(levelname)-8s %(message)s’, level=logging.INFO, datefmt=’%Y-%m-%d %H:%M:%S’) logging.info(‘an info messge’) # 2017-05-25 00:58:28 INFO an info messge logging.debug(‘a debug messag is not shown’) Here is what the above code is Doing: 1. The logging module is imported. 2. The basicConfig() function is called to configure the logging system. 3. The logging.info() function is…
data = Students.objects.all() stu = { “student_number”: data } return render_to_response(“login/profile.html”, stu) // in html file : {% for student in student_number %} {{ student.f_name}} {{ student.l_name }} {{ student.student_number }} {{ student.dob }} {% endfor %} Here is what the above code is Doing: 1. We are getting all the data from the Students…
from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained(“Sentdex/GPyT”) model = AutoModelWithLMHead.from_pretrained(“Sentdex/GPyT”).to(“cuda”) def generate(code, max_length=100): ”’Takes input code, replaces newline chars with , tokenizes, feeds thru model, decodes, then reformats the newlines back in”’ newlinechar = “” converted = code.replace(“\n”, newlinechar) tokenized = tokenizer.encode(converted, return_tensors=’pt’).to(“cuda”) resp = model.generate(tokenized, max_length=max_length).to(“cuda”) decoded = tokenizer.decode(resp[0]) reformatted = decoded.replace(“”,”\n”) return…
An enumeration is a set of symbolic names (members) bound to unique, constant values. Within an enumeration, the members can be compared by identity, and the enumeration itself can be iterated over. Here is what the above code is Doing: 1. We create a new enumeration called Animal. 2. We create three members of the…