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  • List of zeros in python - Stack Overflow
    How can I create a list which contains only zeros? I want to be able to create a zeros list for each int in range(10) For example, if the int in the range was 4 I will get: [0,0,0,0] and for 7:
  • Meaning of list[-1] in Python - Stack Overflow
    I have a piece of code here that is supposed to return the least common element in a list of elements, ordered by commonality: def getSingle(arr): from collections import Counter c = Counte
  • python - How to convert list to string - Stack Overflow
    How can I convert a list to a string using Python?
  • How to list all installed packages and their versions in Python?
    Is there a way in Python to list all installed packages and their versions? I know I can go inside python Lib site-packages and see what files and directories exist, but I find this very awkward
  • What is the difference between list and list [:] in python?
    When reading, list is a reference to the original list, and list[:] shallow-copies the list When assigning, list (re)binds the name and list[:] slice-assigns, replacing what was previously in the list Also, don't use list as a name since it shadows the built-in
  • List all packages installed with winget - Stack Overflow
    Is there a way to list only packages installed specifically with the winget command? winget list seems to show all packages installed on the machine I am changing my computer and I want to get a l
  • slice - How slicing in Python works - Stack Overflow
    The first way works for a list or a string; the second way only works for a list, because slice assignment isn't allowed for strings Other than that I think the only difference is speed: it looks like it's a little faster the first way Try it yourself with timeit timeit () or preferably timeit repeat ()
  • List unique values in a Pandas dataframe - Stack Overflow
    You can get the unique values in the whole df with this one-liner: pd Series(df values flatten()) unique() You basically transform your df to a numpy array, flatten and come back to a pandas Series, so you can use unique() However, types might be transformed along the way if you have multiple types in your original df, so be careful




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