An easy and fun way to permanently store python objects.

Object Bucket

An easy and fun way to store python objects.

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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