An easy and fun way to permanently store python objects.

Object Bucket
An easy and fun way to store python objects.
Star it on GitHub
Description
Object Bucket is a python package that allows you to store python objects permanently in a more user friendly way.
Installation
The object-bucket package can be installed by using pip.
pip install object-bucket
Usage
Creating new bucket.
from object_bucket import Bucket

  test_bucket = Bucket("name-of-the-bucket")
  • Adding droplets to the bucket, droplets are considered as objects that you want to save permanently.
  • test_obj = [1, 2, 3, 4]
      test_bucket.add_droplet("droplet-name", test_obj)
    Trying to add a droplet with the same name will cause an error.
  • Adding multiple droplets. To add multiple droplets you have to have a dictionary that contains all the names and objects of the droplet. To add the dictionary you can use the add_droplets method.
  • droplets = {
            "one": 1,
            "two": 2,
            "three": [2, 3, 4]
        }
    
        test_bucket.add_droplets(droplets)
    Modifying a droplet
    new_obj = {1: "a"}
      test_bucket.modify_droplet("droplet-name", new_obj)
    Trying to modify a droplet that does not exists will cause an error.
    Saving a bucket
    All the things mentioned above will not be added or saved permanently, to do so it is necessary to save the bucket.
    test_bucket.save_bucket()
    Retrieving values from a bucket.
    from object_bucket import Bucket
     test_bucket = Bucket("name-of-the-bucket")
     a = test.bucker.get_droplet("droplet-name")
     print(a)  # {1: "a"}
    Trying to get a droplet that does not exists will cause an error.
  • Get all the runtime droplets
  • drop1 = [1, 2, 3, 4]
     drop2 = "Hello"
     drop3 = {1: "a", 2: "b"}
     test_bucket.add_droplet("drop1", drop1)
     test_bucket.add_droplet("drop2", drop2)
     test_bucket.add_droplet("drop3", drop3)
    
     # to get all the droplets
     a = test_bucket.get_all_droplets()
     print(a)
    
     # output
     {"drop1": [1, 2, 3, 4], "drop2": "Hello", "drop3": {1: "a", 2: "b"}}
  • Deleting a bucket To delete the bucket and to clear the runtime storage of all the droplets.
  • test_bucket.delete_bucket()
  • You can also delete a bucket using remove_bucket function
  • from object_bucket import remove_bucket
      remove_bucket("name-of_bucket_to_be_removed", bucket_file_path="file-path-of-the-bucket")
    Using the context manager.
    It might be a hastle to remember to save to bucket, so you can use the context manager to avoid using the ""save_bucket"" method.
    Note Using ""Bucket().delete_bucket"" inside the context
    manager is useless as at the end the file will be saved automatically.
    from object_bucket import Bucket
    
    with Bucket("name-of-the-bucket") as b:
      # code to execute
      b.add_droplet("name", 1)
      # ...etc
      b.delete_bucket()  # wont work as the file will be again saved,
      # but the runtime contents will be cleared
    Some more stuff
  • You can use the if statement to check whether a bucket is empty or not
  • from object_bucket import Bucket
      t = Bucket("name")
      if t:
        print("Hello")  # -> does not print anything as bucket is empty
    
      t.add_droplet("demo", 1)
      if t:
        print("Hello 2")  # -> prints "hello 2" as the bucket has at least one droplet
  • To get the number of droplets in a bucket you can use the len method
  • from object_bucket import Bucket
      t = Bucket("name")
      print(len(t)) # -> 0
      t.add_droplet("demo", [1, 2, 3])
      print(len(t)) # -> 1
    This will work with any python object, including functions, classes, lambda functions ..etc

    25

    This website collects cookies to deliver better user experience

    An easy and fun way to permanently store python objects.