Series.dt can be used to access series values ​​as datetimelike and return multiple properties. Series.dt.daysinmonth Pandas Series.dt.daysinmonth returns the number of days in a month for a given series object.

Syntax: Series.dt.daysinmonth

Parameter: None

Returns: numpy array

Example # 1: Use the Series.dt.daysinmonth attribute to find the number of days in a month given date in the series object.

# import pandas as pd import pandas as pd   # Create a series sr = pd. Series ([ `2012-12-31` , ` 2019-1-1 12:30` , `2008-02-2 10:30` ,   `2010-1-1 09: 25` , ` 2019-12-31 00:00` ])   # 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.daysinmonth attribute to find the number of days in a month for a given date.

# find number # days in a month result = sr.dt.daysinmonth   # print the result print (result)

Output:

As we can see from the output, the Series.dt.daysinmonth attribute successfully accessed and returned the number of days in the month for the given date.

Example # 2: Use the Series attribute .dt.daysinmonth to find the number of days in the month of a given date in a series object.

# import pandas as pd import pandas as pd < p>   # Create series sr = pd.Series (pd.date_range ( `2012-12- 31 00:00` , periods = 5 , freq = `D` ) )   # Create index idx = [ `Day 1` , `Day 2` , `Day 3` , ` Day 4` , `Day 5` ]   # set index sr.index = idx    # Print series print (sr)

Exit :

Now we will use the Series.dt.daysinmonth attribute to find the number of days in a month for a given date.

# find number # days in a month result = sr.dt.daysinmonth   # print result print (result)

Output:

How we can see from the output that the Series.dt.daysinmonth attribute successfully accessed and returned the number of days in the month for the given date.