Sort_values() With Key In Python

I have a dataframe where the column names are times (0:00, 0:10, 0:20, ..., 23:50). Right now, they're sorted in a string order (so 0:00 is first and 9:50 is last) but I want to so

Solution 1:

Try sorting the columns with the sorted builtin function and passing the output to the dataframe for indexing. The following should serve as a working example:

import pandas as pd


records = [(2, 33, 23, 45), (3, 4, 2, 4), (4, 5, 7, 19), (4, 6, 71, 2)]
df = pd.DataFrame.from_records(records, columns = ('0:00', '23:40', '12:30', '11:23'))
df#    0:00  23:40  12:30  11:23# 0     2     33     23     45# 1     3      4      2      4# 2     4      5      7     19# 3     4      6     71      2df[sorted(df,key=pd.to_datetime)]

#    0:00  11:23  12:30  23:40# 0     2     45     23     33# 1     3      4      2      4# 2     4     19      7      5# 3     4      2     71      6

I hope this helps

Solution 2:

Just prepend a leading zero to one-digit hours. This should be the simplest solution as you can simply sort lexically then.

E.g. 5:30 -> 05:30.

Solution 3:

Here is a working demo, which implements @MartinKrämer's idea:

import re

In [259]: df
Out[259]:
   23:400:0019:1912:30  09:0011:230332123124514312134254171419364171142

In [260]: df.rename(columns=lambda x: re.sub(r'^(\d{1})\:', r'0\1:', x)).sort_index(axis=1)
Out[260]:
   00:00  09:0011:2312:3019:1923:400212452313313134214241419715341427116

Solution 4:

I know this question is a few years old, but since it's the top Google result for this question, I wanted to provide the root cause of the error.

The 'key' argument was added to sort_values in version 1.1.0. See the note in the documentation linked below.

pandas.DataFrame.sort_values

This feature will very like work as you intended if you upgrade to 1.1.0 or higher.