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.

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Installation

The object-bucket package can be installed by using pip.

pip install object-bucket

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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

Comments

[Ice or Fire]

Interesting... what's the benefit of using this over a database?

[Adwaith Rajesh]

well a DB is to store a lot of info. If you are looking for something quick and easy for a small project then you can use this. I've also written blog post on storing object in PysonDB, the same principle can be applied in Storing objects in a JSON file. you can get the post here and moreover the project uses only one dependency that is dill. the reason it doesn't uses pickle is that pickle doesn't support some object like lambda functions. So this is also light weight.