python pandas extract year from datetime df year df date year is not working

Question

I import a dataframe via read csv  but for some reason can t extract the year or month from the series df  date    trying that gives AttributeError   Series  object has no attribute  year   date    Count 6 30 2010   525 7 30 2010   136 8 31 2010   125 9 30 2010   84 10 29 2010  4469  df   pd read csv  sample data csv   parse dates True   df  date     pd to datetime df  date     df  year     df  date   year df  month     df  date   month  UPDATE  and when I try solutions with df  date   dt on my pandas version 0 14 1  I get    quot AttributeError   Series  object has no attribute  dt   quot   df   pd read csv  sample data csv  parse dates True   df  date     pd to datetime df  date     df  year     df  date   dt year df  month     df  date   dt month  Sorry for this question that seems repetitive - I expect the answer will make me feel like a bonehead    but I have not had any luck using answers to the similar questions on SO   FOLLOWUP  I can t seem to update my pandas 0 14 1 to a newer release in my Anaconda environment  each of the attempts below generates an invalid syntax error  I m using Python 3 4 1 64bit  conda update pandas  conda install pandas  0 15 2  conda install -f pandas  Any ideas

User · Answer

What worked for me was upgrading pandas to latest version   From Command Line do   conda update pandas

User · Answer

Probably already too late to answer but since you have already parse the dates while loading the data  you can just do this to get the day     df  date     pd DatetimeIndex df  date    year

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If you re running a recent-ish version of pandas then you can use the datetime attribute dt to access the datetime components   In  6    df  date     pd to datetime df  date    df  year    df  month     df  date   dt year  df  date   dt month df Out 6           date  Count  year  month 0 2010-06-30    525  2010      6 1 2010-07-30    136  2010      7 2 2010-08-31    125  2010      8 3 2010-09-30     84  2010      9 4 2010-10-29   4469  2010     10   EDIT  It looks like you re running an older version of pandas in which case the following would work   In  18    df  date     pd to datetime df  date    df  year    df  month     df  date   apply lambda x  x year   df  date   apply lambda x  x month  df Out 18           date  Count  year  month 0 2010-06-30    525  2010      6 1 2010-07-30    136  2010      7 2 2010-08-31    125  2010      8 3 2010-09-30     84  2010      9 4 2010-10-29   4469  2010     10   Regarding why it didn t parse this into a datetime in read csv you need to pass the ordinal position of your column   0   because when True it tries to parse columns  1 2 3  see the docs  In  20    t    date   Count 6 30 2010   525 7 30 2010   136 8 31 2010   125 9 30 2010   84 10 29 2010  4469    df   pd read csv io StringIO t   sep   s    parse dates  0   df info    lt class  pandas core frame DataFrame  gt  Int64Index  5 entries  0 to 4 Data columns  total 2 columns   date     5 non-null datetime64 ns  Count    5 non-null int64 dtypes  datetime64 ns  1   int64 1  memory usage  120 0 bytes   So if you pass param parse dates  0  to read csv there shouldn t be any need to call to datetime on the  date  column after loading

User · Answer

This works   df  date   dt year   Now   df  year     df  date   dt year df  month     df  date   dt month   gives this data frame           date  Count  year  month 0 2010-06-30    525  2010      6 1 2010-07-30    136  2010      7 2 2010-08-31    125  2010      8 3 2010-09-30     84  2010      9 4 2010-10-29   4469  2010     10

User · Answer

When to use dt accessor  A common source of confusion revolves around when to use  year and when to use  dt year   The former is an attribute for pd DatetimeIndex objects  the latter for pd Series objects  Consider this dataframe   df   pd DataFrame   Dates   pd to datetime   2018-01-01    2018-10-20    2018-12-25                        index pd to datetime   2000-01-01    2000-01-02    2000-01-03       The definition of the series and index look similar  but the pd DataFrame constructor converts them to different types   type df index        pandas tseries index DatetimeIndex type df  Dates       pandas core series Series   The DatetimeIndex object has a direct year attribute  while the Series object must use the dt accessor  Similarly for month   df index month                 array  1  1  1   df  Dates   dt month values    array   1  10  12   dtype int64    A subtle but important difference worth noting is that df index month gives a NumPy array  while df  Dates   dt month gives a Pandas series  Above  we use pd Series values to extract the NumPy array representation

[python] python pandas extract year from datetime: df['year'] = df['date'].year is not working

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