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I have a DataFrame
customer_number purchase_time quantity
14 2007-03-01 07:06:00 10
20 2007-03-12 13:05:00 13
I tried to find the total quantity bought in the morning and afternoon. I converted purchase_time into datetime
df['purchase_time'] = pd.to_datetime(df['purchase_time'])
# Baskets bought in morning.
df[df['purchase_time'] < '12:00:00']
However, the result is original dataset.
597
Comparison between pandas timestamp objects is carried out using simple comparison operators: >, <,==,< = , >=. The difference can be calculated using a simple '–' operator. Given time can be converted to pandas timestamp using pandas. Timestamp() method.
Timestamp is the pandas equivalent of python's Datetime and is interchangeable with it in most cases. It's the type used for the entries that make up a DatetimeIndex, and other timeseries oriented data structures in pandas.
Using pandas datetime properties. Initially, the values in datetime are character strings and do not provide any datetime operations (e.g. extract the year, day of the week,…). By applying the to_datetime function, pandas interprets the strings and convert these to datetime (i.e. datetime64[ns, UTC] ) objects.
You can
df[df['purchase_time'].dt.time < pd.to_datetime('12:00:00').time()]
Out[152]:
customer_number purchase_time quantity
0 14 2007-03-01 07:06:00 10
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