python pandas extract year from datetime: df['year'] = df['date'].year is not working

Question

I import a dataframe via read_csv, but for some reason can't extract the year or month from the series df['date'], trying that gives AttributeError: 'Series' object has no attribute 'year':

date    Count 6/30/2010   525 7/30/2010   136 8/31/2010   125 9/30/2010   84 10/29/2010  4469  df = pd.read_csv('sample_data.csv', parse_dates=True)  df['date'] = pd.to_datetime(df['date'])  df['year'] = df['date'].year df['month'] = df['date'].month 

UPDATE: and when I try solutions with df['date'].dt on my pandas version 0.14.1, I get "AttributeError: 'Series' object has no attribute 'dt' ":

df = pd.read_csv('sample_data.csv',parse_dates=True)  df['date'] = pd.to_datetime(df['date'])  df['year'] = df['date'].dt.year df['month'] = df['date'].dt.month 

Sorry for this question that seems repetitive - I expect the answer will make me feel like a bonehead... but I have not had any luck using answers to the similar questions on SO.

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FOLLOWUP: I can't seem to update my pandas 0.14.1 to a newer release in my Anaconda environment, each of the attempts below generates an invalid syntax error. I'm using Python 3.4.1 64bit.

conda update pandas  conda install pandas==0.15.2  conda install -f pandas 

Any ideas?

Answer 1

If you're running a recent-ish version of pandas then you can use the datetime attribute dt to access the datetime components:

In [6]:  df['date'] = pd.to_datetime(df['date']) df['year'], df['month'] = df['date'].dt.year, df['date'].dt.month df Out[6]:         date  Count  year  month 0 2010-06-30    525  2010      6 1 2010-07-30    136  2010      7 2 2010-08-31    125  2010      8 3 2010-09-30     84  2010      9 4 2010-10-29   4469  2010     10 

EDIT

It looks like you're running an older version of pandas in which case the following would work:

In [18]:  df['date'] = pd.to_datetime(df['date']) df['year'], df['month'] = df['date'].apply(lambda x: x.year), df['date'].apply(lambda x: x.month) df Out[18]:         date  Count  year  month 0 2010-06-30    525  2010      6 1 2010-07-30    136  2010      7 2 2010-08-31    125  2010      8 3 2010-09-30     84  2010      9 4 2010-10-29   4469  2010     10 

Regarding why it didn't parse this into a datetime in read_csv you need to pass the ordinal position of your column ([0]) because when True it tries to parse columns [1,2,3] see the docs

In [20]:  t="""date   Count 6/30/2010   525 7/30/2010   136 8/31/2010   125 9/30/2010   84 10/29/2010  4469""" df = pd.read_csv(io.StringIO(t), sep='\s+', parse_dates=[0]) df.info() <class 'pandas.core.frame.DataFrame'> Int64Index: 5 entries, 0 to 4 Data columns (total 2 columns): date     5 non-null datetime64[ns] Count    5 non-null int64 dtypes: datetime64[ns](1), int64(1) memory usage: 120.0 bytes 

So if you pass param parse_dates=[0] to read_csv there shouldn't be any need to call to_datetime on the 'date' column after loading.

Answer 2

This works:

df['date'].dt.year 

Now:

df['year'] = df['date'].dt.year df['month'] = df['date'].dt.month 

gives this data frame:

        date  Count  year  month 0 2010-06-30    525  2010      6 1 2010-07-30    136  2010      7 2 2010-08-31    125  2010      8 3 2010-09-30     84  2010      9 4 2010-10-29   4469  2010     10