How do I convert strings in a Pandas data frame to a date data type

Question

I have a Pandas data frame  one of the column contains date strings in the format YYYY-MM-DD For e g   2013-10-28  At the moment the dtype of the column is object  How do I convert the column values to Pandas date format

User · Answer

Column          Non-Null Count   Dtype          ---  ------          --------------   -----           0   startDay        110526 non-null  object  1   endDay          110526 non-null  object  import pandas as pd  df  startDay     pd to datetime df startDay   df  endDay     pd to datetime df endDay        Column          Non-Null Count   Dtype          ---  ------          --------------   -----           0   startDay        110526 non-null  datetime64 ns   1   endDay          110526 non-null  datetime64 ns

User · Answer

Try to convert one of the rows into timestamp using the pd to datetime function and then use  map to map the formular to the entire column

User · Answer

Another way to do this and this works well if you have multiple columns to convert to datetime    cols     date1   date2   df cols    df cols  apply pd to datetime

User · Answer

If you want to get the DATE and not DATETIME format   df  id date     pd to datetime df  id date    dt date

User · Answer

Now you can do df  column   dt date  Note that for datetime objects  if you don t see the hour when they re all 00 00 00  that s not pandas  That s iPython notebook trying to make things look pretty

User · Answer

Use astype  In  31   df Out 31       a        time 0  1  2013-01-01 1  2  2013-01-02 2  3  2013-01-03  In  32   df  time     df  time   astype  datetime64 ns     In  33   df Out 33       a                time 0  1 2013-01-01 00 00 00 1  2 2013-01-02 00 00 00 2  3 2013-01-03 00 00 00

User · Answer

I imagine a lot of data comes into Pandas from CSV files  in which case you can simply convert the date during the initial CSV read   dfcsv   pd read csv  xyz csv   parse dates  0   where the 0 refers to the column the date is in  You could also add   index col 0 in there if you want the date to be your index   See https   pandas pydata org pandas-docs stable reference api pandas read csv html

User · Answer

Essentially equivalent to  waitingkuo  but I would use to datetime here  it seems a little cleaner  and offers some additional functionality e g  dayfirst    In  11   df Out 11      a        time 0  1  2013-01-01 1  2  2013-01-02 2  3  2013-01-03  In  12   pd to datetime df  time    Out 12   0   2013-01-01 00 00 00 1   2013-01-02 00 00 00 2   2013-01-03 00 00 00 Name  time  dtype  datetime64 ns   In  13   df  time     pd to datetime df  time     In  14   df Out 14      a                time 0  1 2013-01-01 00 00 00 1  2 2013-01-02 00 00 00 2  3 2013-01-03 00 00 00     Handling ValueErrors If you run into a situation where doing   df  time     pd to datetime df  time      Throws a   ValueError  Unknown string format   That means you have invalid  non-coercible  values  If you are okay with having them converted to pd NaT  you can add an errors  coerce  argument to to datetime   df  time     pd to datetime df  time    errors  coerce

User · Answer

It may be the case that dates need to be converted to a different frequency  In this case  I would suggest setting an index by dates    set an index by dates df set index   time    drop True  inplace True    After this  you can more easily convert to the type of date format you will need most  Below  I sequentially convert to a number of date formats  ultimately ending up with a set of daily dates at the beginning of the month    Convert to daily dates df index   pd DatetimeIndex data df index    Convert to monthly dates df index   df index to period freq  M     Convert to strings df index   df index strftime   Y- m     Convert to daily dates df index   pd DatetimeIndex data df index    For brevity  I don t show that I run the following code after each line above   print df index  print df index dtype  print type df index     This gives me the following output   Index   2013-01-01    2013-01-02    2013-01-03    dtype  object   name  time   object  lt class  pandas core indexes base Index  gt   DatetimeIndex   2013-01-01    2013-01-02    2013-01-03    dtype  datetime64 ns    name  time   freq None  datetime64 ns   lt class  pandas core indexes datetimes DatetimeIndex  gt   PeriodIndex   2013-01    2013-01    2013-01    dtype  period M    name  time   freq  M   period M   lt class  pandas core indexes period PeriodIndex  gt   Index   2013-01    2013-01    2013-01    dtype  object   object  lt class  pandas core indexes base Index  gt   DatetimeIndex   2013-01-01    2013-01-01    2013-01-01    dtype  datetime64 ns    freq None  datetime64 ns   lt class  pandas core indexes datetimes DatetimeIndex  gt

User · Answer

For the sake of completeness  another option  which might not be the most straightforward one  a bit similar to the one proposed by  SSS  but using rather the datetime library is  import datetime df  quot Date quot     df  quot Date quot   apply lambda x  datetime datetime strptime x    Y- d- m   date

[python] How do I convert strings in a Pandas data frame to a 'date' data type?

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