Skip to main content

Utility data structures with simple but powerful features.

Project description

DictKit

A Python package that provides utility data structures with simple but powerful features, with a focus on flexibility and user experience.

UtilDict

A feature-enriched dictionary.

  • Access items with dot notation.
  • Flexible subscripting:
    • Get multiple items at once.
    • Set multiple items or the same value to multiple items at once.
  • Add items without mutating - return an updated copy
  • Drop items without mutating - return a filtered copy
  • Accepts a variety of argument types at creation.
  • Displays in nested format when printed.
  • Easy conversion to json format with .json()

Examples

from dictkit import UtilDict

# Like a dictionary...
ud = UtilDict(a=1, b=2, c=3)
print(ud)  # {'a': 1, 'b': 2, 'c': 3}

# ... but can be initialized from a variety of types
ud = UtilDict({"a": 1}, [("b", 2)], c=3)
print(ud)  # {'a': 1, 'b': 2, 'c': 3}

# Supports dot notation access
print(ud['a'])  # 1
print(ud.a)  # 1

# Get multiple items at once
selected_items = ud[["a", "c"]]
print(selected_items)  # {'a': 1, 'c': 3}

# Set multiple items at once
ud[["a", "c"]] = 10, 30
print(ud)  # {'a': 10, 'b': 2, 'c': 30}

# Set the same value to multiple keys at once
ud[["a", "c"]] = 99
print(ud)  # {'a': 99, 'b': 2, 'c': 99}

# Add items from a variety of types
ud2 = ud.add({"c": 3}, ("d", 4), e=5)
print(ud2)  # {'a': 1, 'b': 2, 'c': 3, 'd': 4, 'e': 5}

# Add items from a 2-column dataframe
import pandas as pd
ud = UtilDict(a=1, b=2)
df = pd.DataFrame({"key": ["c", "d"], "value": [3, 4]})
ud2 = ud.add(df)
print(ud2)  # {'a': 1, 'b': 2, 'c': 3, 'd': 4}

# Drop multiple items at once
>>> ud3 = ud2.drop("a", "c")
>>> print(ud3)  # {'b': 2, 'd': 4}


render()

Somehow, this tool does not yet exist from any popular libraries.

Represents iterables in nested JSON structure, but with Python formatting.

>>> from dictkit.render import render
>>> dct = {'a':1, 'b': {'c':3}, 'list': [int,'b']}
>>> s = render(dct)
>>> s
{
   'a': 1,
   'b': {
      'c': 3
   },
   'list': [
      <class 'int'>,
      'b'
   ]
}

It handles multiline-formatted strings (like dataframes) elegantly, maintaining their original appearance.

>>> from pandas import DataFrame
>>> df = DataFrame([[1, 2, 3], [4, 55, 6]],
...                columns=["ONE", "TWO", "THREE"])
>>> fmt_str = '''|------|
... |      |
... |------|'''
>>> dct = {
...     "key": "value",
...     "formatted string": fmt_str,
...     "nested dct": {
...         "x": "y",
...         "dataframe": df,
...         "a": "b",
...         "formatted string": fmt_str
...     },
...     "lst": ['a', 'b'],
...     "tple": ('a','b')
... }
>>> render(dct, quote=False)
{
   key: value,
   formatted string:
      |------|
      |      |
      |------|,
   nested dct: {
      x: y,
      dataframe:
            ONE  TWO  THREE
         0    1    2      3
         1    4   55      6,
      a: b,
      formatted string:
         |------|
         |      |
         |------|
   },
   lst: [
      a,
      b
   ],
   tple: (
      a,
      b
   )
}

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

dictkit-0.1.6.tar.gz (10.4 kB view hashes)

Uploaded Source

Built Distribution

dictkit-0.1.6-py2.py3-none-any.whl (10.9 kB view hashes)

Uploaded Python 2 Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page