How to convert string to datetime, ignoring time information?

Question

In Python3 and pandas I have a dataframe with a column of strings representing dates - "DataFim" column

df_lotacoes.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 52725 entries, 0 to 52724
Data columns (total 5 columns):
DataFim            48854 non-null object
DataInicio         52725 non-null object
IdUA               52725 non-null object
NomeFuncionario    52725 non-null object
NomeUA             52725 non-null object
dtypes: object(5)
memory usage: 1.0+ MB

print(df_lotacoes['DataFim'])

DataFim
0   2018-11-05T00:00:00-02:00
1   2008-08-28T00:00:00-03:00
2   2002-08-08T00:00:00-03:00
3   2007-03-14T00:00:00-03:00
4   2005-05-06T00:00:00-03:00

I tried to convert to date, but it remains as object

df_lotacoes['DataFim'] = pd.to_datetime(df_lotacoes['DataFim'])

DataFim
0   2018-11-05 00:00:00-02:00
1   2008-08-28 00:00:00-03:00
2   2002-08-08 00:00:00-03:00
3   2007-03-14 00:00:00-03:00
4   2005-05-06 00:00:00-03:00

DataFim            48854 non-null object

I just need the year, month and day information. The other time data I want to ignore

Please, does anyone know how I can convert this format?

Possible duplicate of [Keep only date part when using pandas.to\_datetime](https://stackoverflow.com/questions/16176996/keep-only-date-part-when-using-pandas-to-datetime) — Andreas K., Feb 27 '19 at 20:50

Vaishali · Accepted Answer · 2019-02-27T21:00:29.553

Extract date part using str.extract and convert to datetime,

df['DataFim'] = pd.to_datetime(df['DataFim'].str.extract('(.*)T')[0], format = '%Y-%m-%d')

    DataFim
0   2018-11-05
1   2008-08-28
2   2002-08-08
3   2007-03-14
4   2005-05-06

Option 2: You can also use str.split

df['DataFim'] = pd.to_datetime(df['DataFim'].str.split('T').str[0], format = '%Y-%m-%d')

Having some fun with regex,

df['DataFim'] = pd.to_datetime(df['DataFim'].str.replace('T.*', '', regex = True), format = '%Y-%m-%d')

How to convert string to datetime, ignoring time information?

1 Answers1