CODEWITHSUNDEEP
pythonpandasdatetime
ubuntu_noob
ubuntu_noob

Reputation: 2365

Remove the time from datetime.datetime in pandas column

I have a pandas column called 'date' which has values and type like 2014-07-30 00:00:00 <class 'datetime.datetime'>. I want to remove the time from the date.The end result being `2014-07-30' in datetime.datetime format.

I tried a bunch of solutions like-

df['PSG Date '] = df['PSG Date '].dt.date

but its giving me error-

AttributeError: Can only use .dt accessor with datetimelike values

Upvotes: 5

Views: 12516

Answers (3)

jpp
jpp

Reputation: 164673

First, you should begin with a datetime series; if you don't have one, use pd.to_datetime to force this conversion. This will permit vectorised computations:

df = pd.DataFrame({'col': ['2014-07-30 12:19:22', '2014-07-30 05:52:05',
                           '2014-07-30 20:15:00']})

df['col'] = pd.to_datetime(df['col'])

Next, note you cannot remove time from a datetime series in Pandas. By definition, a datetime series will include both "date" and "time" components.

Normalize time

You can use pd.Series.dt.floor or pd.Series.dt.normalize to reset the time component to 00:00:00:

df['col_floored'] = df['col'].dt.floor('d')
df['col_normalized'] = df['col'].dt.normalize()

print(df['col_floored'].iloc[0])     # 2014-07-30 00:00:00
print(df['col_normalized'].iloc[0])  # 2014-07-30 00:00:00

Convert to datetime.date pointers

You can convert your datetime series to an object series, consisting of datetime.date objects representing dates:

df['col_date'] = df['col'].dt.date
print(df['col_date'].iloc[0])        # 2014-07-30

Since these are not held in a contiguous memory block, operations on df['col_date'] will not be vectorised.

How to check the difference

It's useful to check the dtype for the series we have derived. Notice the one option which "removes" time involves converting your series to object.

Computations will be non-vectorised with such a series, since it consists of pointers to datetime.date objects instead of data in a contiguous memory block.

print(df.dtypes)

col               datetime64[ns]
col_date                  object
col_floored       datetime64[ns]
col_normalized    datetime64[ns]

Upvotes: 4

jezrael
jezrael

Reputation: 862641

I believe need first to_datetime and for dates use dt.date:

df['PSG Date '] = pd.to_datetime(df['PSG Date '], errors='coerce').dt.date

If want datetimes with no times use dt.floor:

df['PSG Date '] = pd.to_datetime(df['PSG Date '], errors='coerce').dt.floor('d')

Upvotes: 2

blue note
blue note

Reputation: 29081

You can convert a datetime.datetime to date time.date by calling the .date() method of the object. eg

 current_datetime = datetime.datetime.now()
 date_only = current_datetime.date()

Upvotes: 0

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