prettyformatter 2.0.13

Description:

prettyformatter 2.0.13

prettyformatter
Pretty formatter enables pretty formatting using aligned and hanging
indents for JSON, dataclasses, named tuples, and any custom formatted
object such as Numpy arrays.
For the full documentation, see
here.
Installation
Windows:
py -m pip install prettyformatter

Unix/MacOS:
python3 -m pip install prettyformatter

Imports
from prettyformatter import PrettyClass, PrettyDataclass
from prettyformatter import pprint, pformat, register

JSON Data
prettyformatter works with JSON data.
batters = [
{"id": "1001", "type": "Regular"},
{"id": "1002", "type": "Chocolate"},
{"id": "1003", "type": "Blueberry"},
{"id": "1004", "type": "Devil's Food"},
]

toppings = [
{"id": "5001", "type": None},
{"id": "5002", "type": "Glazed"},
{"id": "5005", "type": "Sugar"},
{"id": "5007", "type": "Powdered Sugar"},
{"id": "5006", "type": "Chocolate with Sprinkles"},
{"id": "5003", "type": "Chocolate"},
{"id": "5004", "type": "Maple"},
]

data = {"id": "0001", "type": "donut", "name": "Cake", "ppu": 0.55, "batters": batters, "topping": toppings}

pprint:
prettyformatter attempts to compromise between alignment,
readability, and horizontal/vertical compactness.
Support for JSON data is also as easy as pprint(json=True).
from prettyformatter import pprint

pprint(data, json=True)
"""
{
"id" : "0001",
"type" : "donut",
"name" : "Cake",
"ppu" : 0.55,
"batters":
[
{"id": "1001", "type": "Regular"},
{"id": "1002", "type": "Chocolate"},
{"id": "1003", "type": "Blueberry"},
{"id": "1004", "type": "Devil's Food"}
],
"topping":
[
{"id": "5001", "type": None},
{"id": "5002", "type": "Glazed"},
{"id": "5005", "type": "Sugar"},
{"id": "5007", "type": "Powdered Sugar"},
{"id": "5006", "type": "Chocolate with Sprinkles"},
{"id": "5003", "type": "Chocolate"},
{"id": "5004", "type": "Maple"}
]
}
"""

pprint supports the same parameters as print, meaning saving to
files is as easy as file=file.
from prettyformatter import pprint

with open("cake.json", mode="w") as file:
pprint(data, json=True, file=file)

PrettyDataclass
prettyformatter supports dataclasses easily.
@dataclass
class Person(PrettyDataclass):
name: str
birthday: str
phone_number: str
address: str


print(Person("Jane Doe", "2001-01-01", "012-345-6789", "123 Sample St."))
"""
Person(
name=
"Jane Doe",
birthday=
"2001-01-01",
phone_number=
"012-345-6789",
address=
"123 Sample St.",
)
"""

register
Custom formatters for existing classes can be registered.
import numpy as np

@register(np.ndarray)
def pformat_ndarray(obj, specifier, depth, indent, shorten, json):
if json:
return pformat(obj.tolist(), specifier, depth, indent, shorten, json)
with np.printoptions(formatter=dict(all=lambda x: format(x, specifier))):
return repr(obj).replace("\n", "\n" + " " * depth)

pprint(dict.fromkeys("ABC", np.arange(9).reshape(3, 3)))
"""
{
"A":
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]]),
"B":
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]]),
"C":
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]]),
}
"""

pprint(dict.fromkeys("ABC", np.arange(9).reshape(3, 3)), json=True)
"""
{
"A" : [[0, 1, 2], [3, 4, 5], [6, 7, 8]],
"B" : [[0, 1, 2], [3, 4, 5], [6, 7, 8]],
"C" : [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
}
"""

License

For personal and professional use. You cannot resell or redistribute these repositories in their original state.

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