Keep only date part when using pandas to datetime

Question

I use pandas to datetime to parse the dates in my data  Pandas by default represents the dates with datetime64 ns  even though the dates are all daily only  I wonder whether there is an elegant clever way to convert the dates to datetime date or datetime64 D  so that  when I write the data to CSV  the dates are not appended with 00 00 00  I know I can convert the type manually element-by-element    dt to datetime   date   for dt in df dates    But this is really slow since I have many rows and it sort of defeats the purpose of using pandas to datetime  Is there a way to convert the dtype of the entire column at once  Or alternatively  does pandas to datetime support a precision specification so that I can get rid of the time part while working with daily data

User · Answer

Since version 0 15 0 this can now be easily done using  dt to access just the date component  df  just date     df  dates   dt date  The above returns a datetime date dtype  if you want to have a datetime64 then you can just normalize the time component to midnight so it sets all the values to 00 00 00  df  normalised date     df  dates   dt normalize    This keeps the dtype as datetime64  but the display shows just the date value   pandas   dt accessor pandas Series dt

User · Answer

This worked for me on UTC Timestamp  2020-08-19T09 12 57 945888  for di  i in enumerate df  YourColumnName         df  YourColumnName   di    pd Timestamp i

User · Answer

Simple Solution   df  date only     df  date time column   dt date

User · Answer

While I upvoted EdChum s answer  which is the most direct answer to the question the OP posed  it does not really solve the performance problem  it still relies on python datetime objects  and hence any operation on them will be not vectorized - that is  it will be slow    A better performing alternative is to use df  dates   dt floor  d    Strictly speaking  it does not  keep only date part   since it just sets the time to 00 00 00  But it does work as desired by the OP when  for instance    printing to screen saving to csv using the column to groupby       and it is much more efficient  since the operation is vectorized   EDIT  in fact  the answer the OP s would have preferred is probably  recent versions of pandas do not write the time to csv if it is 00 00 00 for all observations

User · Answer

Converting to datetime64 D    df dates values astype  M8 D      Though re-assigning that to a DataFrame col will revert it back to  ns    If you wanted actual datetime date   dt   pd DatetimeIndex df dates  dates   np array  datetime date  date tuple  for date tuple in zip dt year  dt month  dt day

User · Answer

Just giving a more up to date answer in case someone sees this old post    Adding  utc False  when converting to datetime will remove the timezone component and keep only the date in a datetime64 ns  data type    pd to datetime df  Date    utc False    You will be able to save it in excel without getting the error  ValueError  Excel does not support datetimes with timezones  Please ensure that datetimes are timezone unaware before writing to Excel

User · Answer

This is a simple way to extract the date   import pandas as pd  d  2015-01-08 22 44 09   date pd to datetime d  date   print date

User · Answer

Pandas v0 13   Use to csv with date format parameter  Avoid  where possible  converting your datetime64 ns  series to an object dtype series of datetime date objects  The latter  often constructed using pd Series dt date  is stored as an array of pointers and is inefficient relative to a pure NumPy-based series   Since your concern is format when writing to CSV  just use the date format parameter of to csv  For example   df to csv filename  date format   Y- m- d     See Python s strftime directives for formatting conventions

User · Answer

Pandas DatetimeIndex and Series have a method called normalize that does exactly what you want   You can read more about it in this answer   It can be used as ser dt normalize

User · Answer

On tables of  gt 1000000 rows I ve found that these are both fast  with floor just slightly faster  df  mydate     df index floor  d    or df  mydate     df index normalize    If your index has timezones and you don t want those in the result  do  df  mydate     df index tz localize None  floor  d    df index date is many times slower  to datetime   is even worse  Both have the further disadvantage that the results cannot be saved to an hdf store as it does not support type datetime date  Note that I ve used the index as the date source here  if your source is another column  you would need to add  dt  e g  df mycol dt floor  d

User · Answer

I wanted to be able to change the type for a set of columns in a data frame and then remove the time keeping the day  round    floor    ceil   all work  df date columns    df date columns  apply pd to datetime  df date columns    df date columns  apply lambda t  t dt floor  d

[python] Keep only date part when using pandas.to_datetime

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