Description
This is an awesome library!
I have a use case that's not quite covered right now. I often have a giant list of dictionaries, where each dictionary has about ~400 key:value pairs.
(Ex. I want it to ignore the data under ignore_this_key and and_this_one)
tasks = [
{'id': 1, 'name': 1, 'ignore_this_key': 1, 'and_this_one': 1},
{'id': 2, 'name': 2, 'ignore_this_key': 2, 'and_this_one': 2}
]What I'd like to be able to do is pass a list or dictionary of headers and get an output that shows only the keys I specified in the headers (if I include all the keys as columns, it's way too beefy for my terminal)
Ex.
id name
1 1
2 2But right now, if I call tabulate.tabulate(tasks, headers={'id': 'id', 'name': 'name'}), I get:
ignore_this_key and_this_one id name
1 1 1 1
2 2 2 2I wrote a little function that does what I want, but it would be more elegant if it was in tabulate itself:
import tabulate
tasks = [
{'id': 1, 'name': 1, 'ignore_this_key': 1, 'and_this_one': 1},
{'id': 2, 'name': 2, 'ignore_this_key': 2, 'and_this_one': 2}
]
def task_table(task_data, headers):
result_data = [{k: t[k] for k in t if k in headers} for t in task_data]
if not isinstance(headers, dict):
headers = {h: h for h in headers}
return tabulate.tabulate(result_data, headers=headers)
print(task_table(tasks, ['id', 'name']))If this seems reasonable, I'll take a crack at a PR for it 😄.
If there's already a way I can do this, apologies.