How to share a ordering between two lists in Python-CodePudding

i got a list of ids and a lists of dates. Both are single entries of separate pandas dataframe columns. Each date corresponds to an id. Something like:

[852634, 727417, 881231]   [2018-05-29, 2015-11-23, 2019-06-26]

How can i order the dates (ascending or descending, does not matter) and export the same ordering to the IDs?

The wanted result is:

[727417, 852634, 881231]   [ 2015-11-23, 2018-05-29, 2019-06-26]

Thank you in advance for all the suggestions, Alessandro

CodePudding user response：

Use numpy -

l1_key = np.argsort(l1)

l1_sorted = np.array(l1)[l1_key]
l2_sorted = np.array(l2)[l1_key]

Output

print(l1_sorted)
print(l2_sorted)

[727417 852634 881231]
['2015-11-23' '2018-05-29' '2019-06-26']

CodePudding user response：

Zip...

>>> x = [852634, 727417, 881231]
>>> y = ["2018-05-29", "2015-11-23", "2019-06-26"]
>>> list(zip(y, x))
[('2018-05-29', 852634), ('2015-11-23', 727417), ('2019-06-26', 881231)]

sort...

>>> sorted(zip(y,x))
[('2015-11-23', 727417), ('2018-05-29', 852634), ('2019-06-26', 881231)]

and unzip.

>>> [x for _, x in sorted(zip(y,x))]
[727417, 852634, 881231]

This is an example of a general technique called a Schwartzian transform. You decorate the list of IDs you want to sort with the corresponding dates, sort the decorated list, then extract (undecorated) the original values from the result.

CodePudding user response：

you can do list.sort() for the int ids = ids.sort()

you could use datetime to compare the second:

from datetime import datetime
dates = [datetime.strftime(date,"Y%-M%-D%") for date in dates]

CodePudding user response：

If you already have a dataframe, it's probably much easier to .explode() 'em and .sort_values() there before export!

>>> import pandas as pd
>>> df = pd.DataFrame({"ids": [[852634, 727417, 881231], [90,100,110,115]], "dates": [["2018-05-29", "2015-11-23", "2019-06-26"], ["2015-01-01", "2021-01-01", "2020-01-01", "2021-01-01"]]})
>>> df
                        ids                                             dates
0  [852634, 727417, 881231]              [2018-05-29, 2015-11-23, 2019-06-26]
1       [90, 100, 110, 115]  [2015-01-01, 2021-01-01, 2020-01-01, 2021-01-01]
>>> df.explode(["ids", "dates"]).sort_values("dates")
      ids       dates
1      90  2015-01-01
0  727417  2015-11-23
0  852634  2018-05-29
0  881231  2019-06-26
1     110  2020-01-01
1     100  2021-01-01
1     115  2021-01-01
>>> df.explode(["ids", "dates"]).sort_values("dates")["ids"].to_numpy()
array([90, 727417, 852634, 881231, 110, 100, 115], dtype=object)