Series.dt
can be used to access series values as datetimelike and return multiple properties. Series.dt.week
Pandas Series.dt.week
returns an empty array containing the ordinal of the week in the year in base data of this series object.
Syntax: Series.dt.week
Parameter: None
Returns: numpy array
Example # 1: Use the Series.dt.week attribute to return the ordinal week of the year in the underlying data of this Series object.
# import pandas as pd
import
pandas as pd
# Create series
sr
=
pd.Series ([
`20121021 09: 30`
,
` 2019718 12: 30`
,
`2008022 10:30`
,
`2010422 09:25`
,
`2019118 02:22`
])
# Create index
idx
=
[
`Day 1`
,
`Day 2`
,
` Day 3`
,
`Day 4`
,
` Day 5 `
]
#est Browse index
sr.index
=
idx
# Convert baseline data to date and time
sr
=
pd.to_datetime (sr)
# Print series
print
(sr)
Output:
Now we we will use the Series.dt.week
attribute to return the ordinal week of the year in the underlying data of this Series object.

Output:
As we can see from the output, the Series.dt.week
attribute successfully accessed and returned an ordinal the week number of the year in the underlying data of this series object.
Example # 2: Use the Series.dt.week
attribute to return the ordinal week of the year in base data of this Series object.

Output:
Now we will use the Series.dt attribute .week
to return the ordinal of the week of the year in the underlying data of this Series object.

Output:
As we can see from the output, the Series.dt.week
attribute successfully accessed and returned the week ordinal of the year in the underlying data of this series object.