How to Efficiently Extract Year and Month from a Pandas Datetime Column?

Extracting Month and Year Values from Pandas Datetime Column

When working with time series data in a Pandas Dataframe, it's often necessary to extract specific components from datetime values for analysis or visualization purposes. In this case, we aim to extract just the month and year from a column containing pandas.tslib.Timestamp objects.

Several methods have been attempted to extract these values. The resample() method with 'M' frequency fails because it requires a DatetimeIndex or PeriodIndex. The lambda function approach fails due to the absence of the getitem attribute for Timestamp objects.

An elegant solution is to set the index of the Dataframe to the ArrivalDate column. This transforms the datetime values into index labels. Subsequent resampling operations can then be performed using the index:

df.index = df['ArrivalDate']

However, for the purpose of extracting separate year and month values into new columns, a different approach is recommended:

df['year'] = pd.DatetimeIndex(df['ArrivalDate']).year
df['month'] = pd.DatetimeIndex(df['ArrivalDate']).month

Alternatively, the dt accessor can be used for a concise syntax:

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

These operations create new columns named 'year' and 'month' that contain the extracted values. This allows for flexible use of these components for further analysis and manipulation.

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