["python","datetime","pandas"]
Pandas: Return Hour from Datetime Column Directly

Assume I have a DataFrame sales of timestamp values: timestamp sales_office 2014-01-01 09:01:00 Cincinnati 2014-01-01 09:11:00 San Francisco 2014-01-01 15:22:00 Chicago 2014-01-01 19:01:00 Chicago I would like to create a new column time_hour. I can create it by writing a short function as so and using apply() to apply it iteratively: def hr_func(ts): return ts.hour sales['time_hour'] = sales['timestamp'].apply(hr_func) I would then see this result: timestamp sales_office time_hour 2014-01-01 09:01:00 Cincinnati 9 2014-01-01 09:11:00 San Francisco 9 2014-01-01 15:22:00 Chicago 15 2014-01-01 19:01:00 Chicago 19 What I'd like to achieve is some shorter transformation like this (which I know is erroneous but gets at the spirit): sales['time_hour'] = sales['timestamp'].hour Obviously the column is of type Series and as such doesn't have those attributes, but it seems there's a simpler way to make use of matrix operations. Is there a more-direct approach?
Assuming timestamp is the index of the data frame, you can just do the following: hours = sales.index.hour If you want to add that to your sales data frame, just do: import pandas as pd pd.concat([sales, pd.DataFrame(hours, index=sales.index)], axis = 1) Edit: If you have several columns of datetime objects, it's the same process. If you have a column ['date'] in your data frame, and assuming that 'date' has datetime values, you can access the hour from the 'date' as: hours = sales['date'].hour Edit2: If you want to adjust a column in your data frame you have to include dt: sales['datehour'] = sales['date'].dt.hour