how to sort by english date format not american pandas .sort()

Question

    symb                dates
4     BLK  01/03/2014 09:00:00
0     BBR  02/06/2014 09:00:00
21     HZ  02/06/2014 09:00:00
24   OMNI  02/07/2014 09:00:00
31   NOTE           


        

Answer 1

You can use pandas.to_datetime and use the format argument then sort it.

>> df['date'] = pd.to_datetime(df['date'], format='%m/%d/%Y %H:%M:%S')
>> df.sort('date')

               date    symb
0 2014-01-03 09:00:00   BLK
1 2014-02-06 09:00:00   BBR
2 2014-02-06 09:00:00    HZ
3 2014-02-07 09:00:00  OMNI
4 2014-03-04 09:00:00  NOTE
6 2014-04-07 09:00:00   RBY
5 2016-03-04 09:00:00   AMP

Answer 2

You can use to_datetime, for sorting sort_values:

#format mm/dd/YYYY
df['dates'] = pd.to_datetime(df['dates'])
print (df.sort_values('dates'))
    symb               dates
4    BLK 2014-01-03 09:00:00
0    BBR 2014-02-06 09:00:00
21    HZ 2014-02-06 09:00:00
24  OMNI 2014-02-07 09:00:00
31  NOTE 2014-03-04 09:00:00
40   RBY 2014-04-07 09:00:00
65   AMP 2016-03-04 09:00:00

---

#format dd/mm/YYYY
df['dates'] = pd.to_datetime(df['dates'], dayfirst=True)
print (df.sort_values('dates'))
    symb               dates
4    BLK 2014-03-01 09:00:00
31  NOTE 2014-04-03 09:00:00
0    BBR 2014-06-02 09:00:00
21    HZ 2014-06-02 09:00:00
24  OMNI 2014-07-02 09:00:00
40   RBY 2014-07-04 09:00:00
65   AMP 2016-04-03 09:00:00

Another solution is use parameter parse_dates in read_csv, if format dd/mm/YYYY add dayfirst=True:

import pandas as pd
import numpy as np
from pandas.compat import StringIO

temp=u"""symb,dates
BLK,01/03/2014 09:00:00
BBR,02/06/2014 09:00:00
HZ,02/06/2014 09:00:00
OMNI,02/07/2014 09:00:00
NOTE,03/04/2014 09:00:00
AMP,03/04/2016 09:00:00
RBY,04/07/2014 09:00:00"""
#after testing replace 'StringIO(temp)' to 'filename.csv'
df = pd.read_csv(StringIO(temp), parse_dates=['dates'])

print (df)
   symb               dates
0   BLK 2014-01-03 09:00:00
1   BBR 2014-02-06 09:00:00
2    HZ 2014-02-06 09:00:00
3  OMNI 2014-02-07 09:00:00
4  NOTE 2014-03-04 09:00:00
5   AMP 2016-03-04 09:00:00
6   RBY 2014-04-07 09:00:00

print (df.dtypes)
symb             object
dates    datetime64[ns]
dtype: object

print (df.sort_values('dates'))
   symb               dates
0   BLK 2014-01-03 09:00:00
1   BBR 2014-02-06 09:00:00
2    HZ 2014-02-06 09:00:00
3  OMNI 2014-02-07 09:00:00
4  NOTE 2014-03-04 09:00:00
6   RBY 2014-04-07 09:00:00
5   AMP 2016-03-04 09:00:00

---

#after testing replace 'StringIO(temp)' to 'filename.csv'
df = pd.read_csv(StringIO(temp), parse_dates=['dates'], dayfirst=True)

print (df)
   symb               dates
0   BLK 2014-03-01 09:00:00
1   BBR 2014-06-02 09:00:00
2    HZ 2014-06-02 09:00:00
3  OMNI 2014-07-02 09:00:00
4  NOTE 2014-04-03 09:00:00
5   AMP 2016-04-03 09:00:00
6   RBY 2014-07-04 09:00:00

print (df.dtypes)
symb             object
dates    datetime64[ns]
dtype: object

print (df.sort_values('dates'))
   symb               dates
0   BLK 2014-03-01 09:00:00
4  NOTE 2014-04-03 09:00:00
1   BBR 2014-06-02 09:00:00
2    HZ 2014-06-02 09:00:00
3  OMNI 2014-07-02 09:00:00
6   RBY 2014-07-04 09:00:00
5   AMP 2016-04-03 09:00:00

Answer 3

I am not sure how you are getting the data, but if you are importing it from some source such as a CSV you could use pandas.read_csv and set parse_dates=True. The question is what is the type of the dates column? You an easily change them to datelike objects using `dateutil.parse.parse. For example,

import pandas
import dateutil
data = {'symb': ['BLK', 'BBR', 'HZ', 'OMNI', 'NOTE', 'AMP', 'RBY'],
        'dates': ['01/03/2014 09:00:00', '02/06/2014 09:00:00', '02/06/2014 09:00:00',
               '02/07/2014 09:00:00', '03/04/2014 09:00:00', '03/04/2016 09:00:00',
               '04/07/2014 09:00:00']}
df = pandas.DataFrame.from_dict(data)
df.dates = df.dates.apply(dateutil.parser.parse)
print df.to_string()

# OUTPUT
# 0 2014-01-03 09:00:00   BLK
# 1 2014-02-06 09:00:00   BBR
# 2 2014-02-06 09:00:00    HZ
# 3 2014-02-07 09:00:00  OMNI
# 4 2014-03-04 09:00:00  NOTE
# 5 2016-03-04 09:00:00   AMP
# 6 2014-04-07 09:00:00   RBY

This gets you the [ISO8601 format] which may be preferable to the dd/mm/yyyy format, but if you must have that format you can use the code recommended by @umutto