How extract only date from datetime column in python?

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?

jpp

152k32 gold badges256 silver badges319 bronze badges

asked Apr 23, 2013 at 18:50

0

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

answered Dec 14, 2015 at 22:07

EdChumEdChum

352k194 gold badges776 silver badges548 bronze badges

3

Simple Solution:

df['date_only'] = df['date_time_column'].dt.date

answered May 20, 2019 at 10:11

Gil BaggioGil Baggio

11.7k3 gold badges47 silver badges34 bronze badges

1

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".

answered Jan 18, 2017 at 11:57

Pietro BattistonPietro Battiston

7,3873 gold badges39 silver badges44 bronze badges

5

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.

answered Dec 14, 2018 at 0:19

jppjpp

152k32 gold badges256 silver badges319 bronze badges

0

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[]

Asclepius

52.2k15 gold badges151 silver badges131 bronze badges

answered Nov 2, 2016 at 12:31

j08luej08lue

1,5872 gold badges20 silver badges35 bronze badges

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]

Nic Scozzaro

5,6351 gold badge35 silver badges44 bronze badges

answered Dec 11, 2017 at 12:02

2

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."

answered Nov 17, 2019 at 19:16

KatekarinKatekarin

1841 silver badge8 bronze badges

2

df['Column'] = df['Column'].dt.strftime['%m/%d/%Y']

This will give you just the dates and NO TIME at your desired format. You can change format according to your need '%m/%d/%Y' It will change the data type of the column to 'object'.

If you want just the dates and DO NOT want time in YYYY-MM-DD format use :

df['Column'] = pd.to_datetime[df['Column']].dt.date

The datatype will be 'object'.

For 'datetime64' datatype, use:

df['Column'] = pd.to_datetime[df['Column']].dt.normalize[]

answered Aug 5, 2021 at 19:43

Talha TayyabTalha Tayyab

2,32815 gold badges16 silver badges28 bronze badges

1

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]]]

answered Apr 23, 2013 at 20:07

DaleDale

3,9601 gold badge18 silver badges13 bronze badges

3

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']]

answered May 4, 2020 at 22:20

On tables of >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']

answered Nov 30, 2020 at 13:02

fantabolousfantabolous

19.7k6 gold badges52 silver badges47 bronze badges

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]

answered Sep 8, 2020 at 9:45

PunnerudPunnerud

6,0022 gold badges47 silver badges41 bronze badges

How do I extract only the date from a datetime object in Python?

How can I get only the date from a timestamp object?

How do I separate a date in a column in Python?

How do I separate date and time from datetime in Python?