# 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
```python
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}
```
---
<br>
## `render()`
> Somehow, this tool does not yet exist from any popular libraries.
### Represents iterables in nested **JSON _structure_**, but with **Python _formatting_**.
```py
>>> 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.
```py
>>> 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
)
}
```
