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

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.

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

When to use dt accessor

A common source of confusion revolves around when to use .year and when to use .dt.year.

The former is an attribute for pd.DatetimeIndex objects; the latter for pd.Series objects. Consider this dataframe:

df = pd.DataFrame({'Dates': pd.to_datetime(['2018-01-01', '2018-10-20', '2018-12-25'])},
                  index=pd.to_datetime(['2000-01-01', '2000-01-02', '2000-01-03']))

The definition of the series and index look similar, but the pd.DataFrame constructor converts them to different types:

type(df.index)     # pandas.tseries.index.DatetimeIndex
type(df['Dates'])  # pandas.core.series.Series

The DatetimeIndex object has a direct year attribute, while the Series object must use the dt accessor. Similarly for month:

df.index.month               # array([1, 1, 1])
df['Dates'].dt.month.values  # array([ 1, 10, 12], dtype=int64)

A subtle but important difference worth noting is that df.index.month gives a NumPy array, while df['Dates'].dt.month gives a Pandas series. Above, we use pd.Series.values to extract the NumPy array representation.