How to encode time in the range of 1 to 48












-1















I have following time format with time interval of half hour in 24 HOUR, i.e 48 intervals in a day.



enter image description here



And I want to encode it in following format.



enter image description here










share|improve this question



























    -1















    I have following time format with time interval of half hour in 24 HOUR, i.e 48 intervals in a day.



    enter image description here



    And I want to encode it in following format.



    enter image description here










    share|improve this question

























      -1












      -1








      -1








      I have following time format with time interval of half hour in 24 HOUR, i.e 48 intervals in a day.



      enter image description here



      And I want to encode it in following format.



      enter image description here










      share|improve this question














      I have following time format with time interval of half hour in 24 HOUR, i.e 48 intervals in a day.



      enter image description here



      And I want to encode it in following format.



      enter image description here







      python-3.x pandas datetime time-series






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked Nov 24 '18 at 6:37









      Nikhil MangireNikhil Mangire

      659




      659
























          1 Answer
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          1














          I believe you need map by dictionary created by timedelta_range and zip, for same types of data is necessary convert column to_timedelta:



          df = pd.DataFrame({'Time':['00:00:00','00:30:00','01:00:00','08:00:00']})

          print (type(df['Time'].iat[0]))
          <class 'str'>

          d = dict(zip(pd.timedelta_range('00:00:00', '23:30:00', freq='30T'), range(1, 49)))
          df['EncodedTime'] = pd.to_timedelta(df['Time']).map(d)
          print (df)
          Time EncodedTime
          0 00:00:00 1
          1 00:30:00 2
          2 01:00:00 3
          3 08:00:00 17


          Similar solution created by date_range with DatetimeIndex.strftime:



          d = dict(zip(pd.date_range('00:00:00', '23:30:00', freq='30T').strftime('%H:%M:%S'), 
          range(1, 49)))
          df['EncodedTime'] = df['Time'].map(d)




          Solution working with times:



          df = pd.DataFrame({'Time':pd.to_datetime(['00:00:00','00:30:00','01:00:00','08:00:00']).time})

          print (type(df['Time'].iat[0]))
          <class 'datetime.time'>

          d = dict(zip(pd.date_range('00:00:00', '23:30:00', freq='30T').time, range(1, 49)))
          df['EncodedTime'] = df['Time'].map(d)





          share|improve this answer


























          • your code only numbers from 1 to ~ , it doesn't encode from 1 to 48.

            – Nikhil Mangire
            Nov 24 '18 at 6:45











          • @NikhilMangire - OK, so check another solution.

            – jezrael
            Nov 24 '18 at 6:47











          • @NikhilMangire - Added next 2 solutions, it still some problem, what return print (type(df['Time'].iat[0])) ?

            – jezrael
            Nov 24 '18 at 7:00











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          1 Answer
          1






          active

          oldest

          votes








          1 Answer
          1






          active

          oldest

          votes









          active

          oldest

          votes






          active

          oldest

          votes









          1














          I believe you need map by dictionary created by timedelta_range and zip, for same types of data is necessary convert column to_timedelta:



          df = pd.DataFrame({'Time':['00:00:00','00:30:00','01:00:00','08:00:00']})

          print (type(df['Time'].iat[0]))
          <class 'str'>

          d = dict(zip(pd.timedelta_range('00:00:00', '23:30:00', freq='30T'), range(1, 49)))
          df['EncodedTime'] = pd.to_timedelta(df['Time']).map(d)
          print (df)
          Time EncodedTime
          0 00:00:00 1
          1 00:30:00 2
          2 01:00:00 3
          3 08:00:00 17


          Similar solution created by date_range with DatetimeIndex.strftime:



          d = dict(zip(pd.date_range('00:00:00', '23:30:00', freq='30T').strftime('%H:%M:%S'), 
          range(1, 49)))
          df['EncodedTime'] = df['Time'].map(d)




          Solution working with times:



          df = pd.DataFrame({'Time':pd.to_datetime(['00:00:00','00:30:00','01:00:00','08:00:00']).time})

          print (type(df['Time'].iat[0]))
          <class 'datetime.time'>

          d = dict(zip(pd.date_range('00:00:00', '23:30:00', freq='30T').time, range(1, 49)))
          df['EncodedTime'] = df['Time'].map(d)





          share|improve this answer


























          • your code only numbers from 1 to ~ , it doesn't encode from 1 to 48.

            – Nikhil Mangire
            Nov 24 '18 at 6:45











          • @NikhilMangire - OK, so check another solution.

            – jezrael
            Nov 24 '18 at 6:47











          • @NikhilMangire - Added next 2 solutions, it still some problem, what return print (type(df['Time'].iat[0])) ?

            – jezrael
            Nov 24 '18 at 7:00
















          1














          I believe you need map by dictionary created by timedelta_range and zip, for same types of data is necessary convert column to_timedelta:



          df = pd.DataFrame({'Time':['00:00:00','00:30:00','01:00:00','08:00:00']})

          print (type(df['Time'].iat[0]))
          <class 'str'>

          d = dict(zip(pd.timedelta_range('00:00:00', '23:30:00', freq='30T'), range(1, 49)))
          df['EncodedTime'] = pd.to_timedelta(df['Time']).map(d)
          print (df)
          Time EncodedTime
          0 00:00:00 1
          1 00:30:00 2
          2 01:00:00 3
          3 08:00:00 17


          Similar solution created by date_range with DatetimeIndex.strftime:



          d = dict(zip(pd.date_range('00:00:00', '23:30:00', freq='30T').strftime('%H:%M:%S'), 
          range(1, 49)))
          df['EncodedTime'] = df['Time'].map(d)




          Solution working with times:



          df = pd.DataFrame({'Time':pd.to_datetime(['00:00:00','00:30:00','01:00:00','08:00:00']).time})

          print (type(df['Time'].iat[0]))
          <class 'datetime.time'>

          d = dict(zip(pd.date_range('00:00:00', '23:30:00', freq='30T').time, range(1, 49)))
          df['EncodedTime'] = df['Time'].map(d)





          share|improve this answer


























          • your code only numbers from 1 to ~ , it doesn't encode from 1 to 48.

            – Nikhil Mangire
            Nov 24 '18 at 6:45











          • @NikhilMangire - OK, so check another solution.

            – jezrael
            Nov 24 '18 at 6:47











          • @NikhilMangire - Added next 2 solutions, it still some problem, what return print (type(df['Time'].iat[0])) ?

            – jezrael
            Nov 24 '18 at 7:00














          1












          1








          1







          I believe you need map by dictionary created by timedelta_range and zip, for same types of data is necessary convert column to_timedelta:



          df = pd.DataFrame({'Time':['00:00:00','00:30:00','01:00:00','08:00:00']})

          print (type(df['Time'].iat[0]))
          <class 'str'>

          d = dict(zip(pd.timedelta_range('00:00:00', '23:30:00', freq='30T'), range(1, 49)))
          df['EncodedTime'] = pd.to_timedelta(df['Time']).map(d)
          print (df)
          Time EncodedTime
          0 00:00:00 1
          1 00:30:00 2
          2 01:00:00 3
          3 08:00:00 17


          Similar solution created by date_range with DatetimeIndex.strftime:



          d = dict(zip(pd.date_range('00:00:00', '23:30:00', freq='30T').strftime('%H:%M:%S'), 
          range(1, 49)))
          df['EncodedTime'] = df['Time'].map(d)




          Solution working with times:



          df = pd.DataFrame({'Time':pd.to_datetime(['00:00:00','00:30:00','01:00:00','08:00:00']).time})

          print (type(df['Time'].iat[0]))
          <class 'datetime.time'>

          d = dict(zip(pd.date_range('00:00:00', '23:30:00', freq='30T').time, range(1, 49)))
          df['EncodedTime'] = df['Time'].map(d)





          share|improve this answer















          I believe you need map by dictionary created by timedelta_range and zip, for same types of data is necessary convert column to_timedelta:



          df = pd.DataFrame({'Time':['00:00:00','00:30:00','01:00:00','08:00:00']})

          print (type(df['Time'].iat[0]))
          <class 'str'>

          d = dict(zip(pd.timedelta_range('00:00:00', '23:30:00', freq='30T'), range(1, 49)))
          df['EncodedTime'] = pd.to_timedelta(df['Time']).map(d)
          print (df)
          Time EncodedTime
          0 00:00:00 1
          1 00:30:00 2
          2 01:00:00 3
          3 08:00:00 17


          Similar solution created by date_range with DatetimeIndex.strftime:



          d = dict(zip(pd.date_range('00:00:00', '23:30:00', freq='30T').strftime('%H:%M:%S'), 
          range(1, 49)))
          df['EncodedTime'] = df['Time'].map(d)




          Solution working with times:



          df = pd.DataFrame({'Time':pd.to_datetime(['00:00:00','00:30:00','01:00:00','08:00:00']).time})

          print (type(df['Time'].iat[0]))
          <class 'datetime.time'>

          d = dict(zip(pd.date_range('00:00:00', '23:30:00', freq='30T').time, range(1, 49)))
          df['EncodedTime'] = df['Time'].map(d)






          share|improve this answer














          share|improve this answer



          share|improve this answer








          edited Nov 24 '18 at 6:56

























          answered Nov 24 '18 at 6:40









          jezraeljezrael

          339k25294365




          339k25294365













          • your code only numbers from 1 to ~ , it doesn't encode from 1 to 48.

            – Nikhil Mangire
            Nov 24 '18 at 6:45











          • @NikhilMangire - OK, so check another solution.

            – jezrael
            Nov 24 '18 at 6:47











          • @NikhilMangire - Added next 2 solutions, it still some problem, what return print (type(df['Time'].iat[0])) ?

            – jezrael
            Nov 24 '18 at 7:00



















          • your code only numbers from 1 to ~ , it doesn't encode from 1 to 48.

            – Nikhil Mangire
            Nov 24 '18 at 6:45











          • @NikhilMangire - OK, so check another solution.

            – jezrael
            Nov 24 '18 at 6:47











          • @NikhilMangire - Added next 2 solutions, it still some problem, what return print (type(df['Time'].iat[0])) ?

            – jezrael
            Nov 24 '18 at 7:00

















          your code only numbers from 1 to ~ , it doesn't encode from 1 to 48.

          – Nikhil Mangire
          Nov 24 '18 at 6:45





          your code only numbers from 1 to ~ , it doesn't encode from 1 to 48.

          – Nikhil Mangire
          Nov 24 '18 at 6:45













          @NikhilMangire - OK, so check another solution.

          – jezrael
          Nov 24 '18 at 6:47





          @NikhilMangire - OK, so check another solution.

          – jezrael
          Nov 24 '18 at 6:47













          @NikhilMangire - Added next 2 solutions, it still some problem, what return print (type(df['Time'].iat[0])) ?

          – jezrael
          Nov 24 '18 at 7:00





          @NikhilMangire - Added next 2 solutions, it still some problem, what return print (type(df['Time'].iat[0])) ?

          – jezrael
          Nov 24 '18 at 7:00




















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