Converting a datetime column to a string column

Question

I'm trying to convert a datetime column back to a string in Pandas dataframe.

the syntax I have so far is:

all_data['Order Day new'] = dt.date.strftime(all_data['Order Day new'], '%d/%m/%Y') 

but this returns the error:

descriptor 'strftime' requires a 'datetime.date' object but received a 'Series'.

Can anyone tell me where I'm going wrong.

Answer 1

If you're using version 0.17.0 or higher then you can call this using .dt.strftime which is vectorised:

all_data['Order Day new'] = all_data['Order Day new'].dt.strftime('%Y-%m-%d') 

** If your pandas version is older than 0.17.0 then you have to call apply and pass the data to strftime:

In [111]:  all_data = pd.DataFrame({'Order Day new':[dt.datetime(2014,5,9), dt.datetime(2012,6,19)]}) print(all_data) all_data.info()   Order Day new 0    2014-05-09 1    2012-06-19 <class 'pandas.core.frame.DataFrame'> Int64Index: 2 entries, 0 to 1 Data columns (total 1 columns): Order Day new    2 non-null datetime64[ns] dtypes: datetime64[ns](1) memory usage: 32.0 bytes  In [108]:  all_data['Order Day new'] = all_data['Order Day new'].apply(lambda x: dt.datetime.strftime(x, '%Y-%m-%d')) all_data Out[108]:   Order Day new 0    2014-05-09 1    2012-06-19 In [109]:  all_data.info() <class 'pandas.core.frame.DataFrame'> Int64Index: 2 entries, 0 to 1 Data columns (total 1 columns): Order Day new    2 non-null object dtypes: object(1) memory usage: 32.0+ bytes 

You can't call strftime on the column as it doesn't understand Series as a param hence the error

Answer 2

all_data['Order Day new']=all_data['Order Day new'].astype(str) 

I think this is more simple, if the date is already in the format you want it in string form.