Series.dt
can be used to access series values as datetimelike and return multiple properties. Series.dt.weekofyear
Pandas Series.dt.weekofyear
returns an empty array containing the ordinal of the week in the year in base data of this series object.
Syntax: Series.dt.weekofyear
Parameter: None
Returns: numpy array
Example # 1: Use the Series.dt.weekofyear 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`
,
` 20197 18 12: 30`
,
`2008022 10: 30`
,
` 2010422 09:25`
,
`2019118 02:22`
])
# Create index
idx
=
[
` Day 1`
,
`Day 2`
,
` Day 3`
,
`Day 4`
,
`Day 5`
]
# set index
sr.index
=
idx
# Convert base data to date and time
sr
=
pd.to_datetime ( sr)
# Print series
print
(sr)
Output:
We will now use the Series.dt.weekofyear
attribute to return the week ordinal of the year in the underlying data of this Series object.
# return the ordinal number of the week
# of the year
result
=
sr.dt.weekofyear
# print the result
print
(result)
Output:
As we can see from the output, the Series.dt.weekofyear attribute
successfully accessed and returned the week of the year in the underlying data of this series object.
Example # 2: Use the Series.dt.weekofyear 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 a series
sr
=
pd.Series (pd.date_range (
`20121212 12: 12`
,
periods
=
5
, freq
=
`M`
))
# Create index
idx
=
[
`Day 1`
,
`Day 2` < / code> ,
`Day 3`
,
` Day 4`
,
`Day 5`
]
# set index
sr.index
=
idx
# Print series
print
(sr)
Output:
Now we will use the Series.dt attribute .weekofyear
to return the week ordinal of the year in the underlying data of this Series object.
Output:
As we can see from the output, the Series.dt.weekofyear
attribute successfully accessed and returned the ordinal of the week of the year in the underlying data of this series object.
