I have a data frame with a column 'Date' with data type datetime64. The values are in YYYY-MM-DD format.
How can I convert it to YYYY-MM format and use it as a datetime64 object itself.
I tried converting my datetime object to a string in YYYY-MM format and then back to datetime object in YYYY-MM format but it didn't work.
Original data = 1988-01-01.
Converting datatime object to string in YY-MM format
df['Date']=df['Date'].dt.strftime('%Y-%m')
This worked as expected, my column value became
1988-01
Converting the string back to datetime object in Y-m format
df['Date']=pd.to_datetime(df['Date'],format= '%Y-%m')
I was expecting the Date column in YYYY-MM format but it became YYYY-MM-DD format.
1988-01-01
Can you please let me know if I am missing something.
Thanks
It is expected behaviour, in datetimes the year, month and day arguments are required.
If want remove days need month period by to_period:
df['Date'] = df['Date'].dt.to_period('M')
df['Date'] = pd.to_datetime(df['Date'],format= '%Y-%m').dt.to_period('M')
Sample:
df = pd.DataFrame({'Date':pd.to_datetime(['1988-01-01','1999-01-15'])})
print (df)
Date
0 1988-01-01
1 1999-01-15
df['Date'] = df['Date'].dt.to_period('M')
print (df)
Date
0 1988-01
1 1999-01
Related
i want to convert Month and year to YYYY-MM-DD in a dataframe in panda, the date will be the first day of that month
i try using this
pd.to_datetime(df, format='%Y-%m-%d', errors='ignore')
I expected the result to be
Try with format '%b,%Y':
df['date']=pd.to_datetime(df['date'], format='%b,%Y', errors='coerce')
OR
Don't use format at all and let pandas infer it:
df['date']=pd.to_datetime(df['date'], errors='coerce')
For more info regarding format code see docs
i have a variable consisting of 300k records with dates and the date look like
2015-02-21 12:08:51
from that date i want to remove time
type of date variable is pandas.core.series.series
This is the way i tried
from datetime import datetime,date
date_str = textdata['vfreceiveddate']
format_string = "%Y-%m-%d"
then = datetime.strftime(date_str,format_string)
some Random ERROR
In the above code textdata is my datasetname and vfreceived date is a variable consisting of dates
How can i write the code to remove the time from the datetime.
Assuming all your datetime strings are in a similar format then just convert them to datetime using to_datetime and then call the dt.date attribute to get just the date portion:
In [37]:
df = pd.DataFrame({'date':['2015-02-21 12:08:51']})
df
Out[37]:
date
0 2015-02-21 12:08:51
In [39]:
df['date'] = pd.to_datetime(df['date']).dt.date
df
Out[39]:
date
0 2015-02-21
EDIT
If you just want to change the display and not the dtype then you can call dt.normalize:
In[10]:
df['date'] = pd.to_datetime(df['date']).dt.normalize()
df
Out[10]:
date
0 2015-02-21
You can see that the dtype remains as datetime:
In[11]:
df.dtypes
Out[11]:
date datetime64[ns]
dtype: object
You're calling datetime.datetime.strftime, which requires as its first argument a datetime.datetime instance, because it's an unbound method; but you're passing it a string instead of a datetime instance, whence the obvious error.
You can work purely at a string level if that's the result you want; with the data you give as an example, date_str.split()[0] for example would be exactly the 2015-02-21 string you appear to require.
Or, you can use datetime, but then you need to parse the string first, not format it -- hence, strptime, not strftime:
dt = datetime.strptime(date_str, '%Y-%m-%d %H:%M:%S')
date = dt.date()
if it's a datetime.date object you want (but if all you want is the string form of the date, such an approach might be "overkill":-).
simply writing
date.strftime("%d-%m-%Y") will remove the Hour min & sec
I have a dataframe with date information in one column.
The date visually appears in the dataframe in this format: 2019-11-24
but when you print the type it shows up as:
Timestamp('2019-11-24 00:00:00')
I'd like to convert each value in the dataframe to a format like this:
24-Nov
or
7-Nov
for single digit days.
I've tried using various datetime and strptime commands to convert but I am getting errors.
Here's a way to do:
df = pd.DataFrame({'date': ["2014-10-23","2016-09-08"]})
df['date_new'] = pd.to_datetime(df['date'])
df['date_new'] = df['date_new'].dt.strftime("%d-%b")
date date_new
0 2014-10-23 23-Oct
1 2016-09-08 08-Sept
Is there a way in pandas to convert my column date which has the following format '1997-01-31' to '199701', without including any information about the day?
I tried solution of the following form:
df['DATE'] = df['DATE'].apply(lambda x: datetime.strptime(x, '%Y%m'))
but I obtain this error : 'ValueError: time data '1997-01-31' does not match format '%Y%m''
Probably the reason is that I am not including the day in the format. Is there a way better to pass from YYYY-MM_DD format to YYYYMM in pandas?
One way is to convert the date to date time and then use strftime. Just a note that you do lose the datetime functionality of the date
df = pd.DataFrame({'date':['1997-01-31' ]})
df['date'] = pd.to_datetime(df['date'])
df['date'] = df['date'].dt.strftime('%Y%m')
date
0 199701
Might not need to go through the datetime conversion if the data are sufficiently clean (no incorrect strings like 'foo' or '001231'):
df = pd.DataFrame({'date':['1997-01-31', '1997-03-31', '1997-12-18']})
df['date'] = [''.join(x.split('-')[0:2]) for x in df.date]
# date
#0 199701
#1 199703
#2 199712
Or if you have null values:
df['date'] = df.date.str.replace('-', '').str[0:6]
I have a particular format of date in my dataframe as
df:
Date
12-Jun-16
22-Jan-12
I want to covert it to this format
df:
Date
12-Jan-2015
Any help as to how to do it?
I think you need convert column to_datetime and then if need change format add strftime:
df.Date = pd.to_datetime(df.Date).dt.strftime('%d-%b-%Y')
print (df)
Date
0 12-Jun-2016
1 22-Jan-2012