Display all the Sundays of given year using Pandas in Python
Letβs see how to display all the Sundays of a given year using Pandas. We will be using the date_range()
function of the Pandas
module.
Algorithm :
- Import the
pandas
module. - Fetch all the Sundays using the
date_range()
function, the parameters are :- In order to display Sundays of 2020,
start
parameter is set as 2020-01-01. - The parameter
periods
is set to 52 as there are approximately 52 weeks in a year. - The parameter
freq
is set to W-SUN where W refers to weekly and SUN refers to Sunday.
- In order to display Sundays of 2020,
- Print the fetched
DateTimeIndex
object.
# importing the module import pandas as pd # target year year = "2020" # instantiating the parameters start = year + "-01-01" periods = 52 freq = "W-SUN" # fetching the Sundays sundays = pd.date_range(start = start, periods = periods, freq = freq) # printing the Sundays print (sundays) |
Output :
DatetimeIndex([β2020-01-05β, β2020-01-12β, β2020-01-19β, β2020-01-26β,
β2020-02-02β, β2020-02-09β, β2020-02-16β, β2020-02-23β,
β2020-03-01β, β2020-03-08β, β2020-03-15β, β2020-03-22β,
β2020-03-29β, β2020-04-05β, β2020-04-12β, β2020-04-19β,
β2020-04-26β, β2020-05-03β, β2020-05-10β, β2020-05-17β,
β2020-05-24β, β2020-05-31β, β2020-06-07β, β2020-06-14β,
β2020-06-21β, β2020-06-28β, β2020-07-05β, β2020-07-12β,
β2020-07-19β, β2020-07-26β, β2020-08-02β, β2020-08-09β,
β2020-08-16β, β2020-08-23β, β2020-08-30β, β2020-09-06β,
β2020-09-13β, β2020-09-20β, β2020-09-27β, β2020-10-04β,
β2020-10-11β, β2020-10-18β, β2020-10-25β, β2020-11-01β,
β2020-11-08β, β2020-11-15β, β2020-11-22β, β2020-11-29β,
β2020-12-06β, β2020-12-13β, β2020-12-20β, β2020-12-27β²],
dtype=βdatetime64[ns]β, freq=βW-SUNβ)
If we want to fetch any other day instead of Sunday, we can tweak the above program by changing the parameter freq
to the desired day.
# importing the module import pandas as pd # target year year = "2020" # day to be fetched day = "MON" # instantiating the parameters start = year + "-01-01" periods = 52 freq = "W-" + day # fetching the days days = pd.date_range(start = start, periods = periods, freq = freq) # printing the days print (days) |
Output :
DatetimeIndex([β2020-01-06β, β2020-01-13β, β2020-01-20β, β2020-01-27β,
β2020-02-03β, β2020-02-10β, β2020-02-17β, β2020-02-24β,
β2020-03-02β, β2020-03-09β, β2020-03-16β, β2020-03-23β,
β2020-03-30β, β2020-04-06β, β2020-04-13β, β2020-04-20β,
β2020-04-27β, β2020-05-04β, β2020-05-11β, β2020-05-18β,
β2020-05-25β, β2020-06-01β, β2020-06-08β, β2020-06-15β,
β2020-06-22β, β2020-06-29β, β2020-07-06β, β2020-07-13β,
β2020-07-20β, β2020-07-27β, β2020-08-03β, β2020-08-10β,
β2020-08-17β, β2020-08-24β, β2020-08-31β, β2020-09-07β,
β2020-09-14β, β2020-09-21β, β2020-09-28β, β2020-10-05β,
β2020-10-12β, β2020-10-19β, β2020-10-26β, β2020-11-02β,
β2020-11-09β, β2020-11-16β, β2020-11-23β, β2020-11-30β,
β2020-12-07β, β2020-12-14β, β2020-12-21β, β2020-12-28β²],
dtype=βdatetime64[ns]β, freq=βW-MONβ)
We may convert the DateTimeIndex
object to a Series object to get a list of the days to be fetched.
# importing the module import pandas as pd # target year year = "2020" # day to be fetched day = "WED" # instantiating the parameters start = year + "-01-01" periods = 52 freq = "W-" + day # fetching the days days = pd.Series(pd.date_range(start = start, periods = periods, freq = freq)) # printing the days print (days) |
Output :
0 2020-01-01 1 2020-01-08 2 2020-01-15 3 2020-01-22 4 2020-01-29 5 2020-02-05 6 2020-02-12 7 2020-02-19 8 2020-02-26 9 2020-03-04 10 2020-03-11 11 2020-03-18 12 2020-03-25 13 2020-04-01 14 2020-04-08 15 2020-04-15 16 2020-04-22 17 2020-04-29 18 2020-05-06 19 2020-05-13 20 2020-05-20 21 2020-05-27 22 2020-06-03 23 2020-06-10 24 2020-06-17 25 2020-06-24 26 2020-07-01 27 2020-07-08 28 2020-07-15 29 2020-07-22 30 2020-07-29 31 2020-08-05 32 2020-08-12 33 2020-08-19 34 2020-08-26 35 2020-09-02 36 2020-09-09 37 2020-09-16 38 2020-09-23 39 2020-09-30 40 2020-10-07 41 2020-10-14 42 2020-10-21 43 2020-10-28 44 2020-11-04 45 2020-11-11 46 2020-11-18 47 2020-11-25 48 2020-12-02 49 2020-12-09 50 2020-12-16 51 2020-12-23 dtype: datetime64[ns]
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