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I'm trying to learn datetime and I'm currently trying to display all dates of November in an html template, in views I have:
year = today.year
month= today.month
num_days = calendar.monthrange(year, month)[1]
days = [datetime.date(year, month, day) for day in range(1, num_days+1)]
for days in days:
days_str = days.strftime('%A, %B, %d, %Y')
print(days_str)
context = {'': }
return render(request, 'template.html', context)
The output of the above is:
Monday, November, 01, 2021
Tuesday, November, 02, 2021
Wednesday, November, 03, 2021
Thursday, November, 04, 2021
Friday, November, 05, 2021
Saturday, November, 06, 2021
Sunday, November, 07, 2021
Monday, November, 08, 2021
Tuesday, November, 09, 2021
Wednesday, November, 10, 2021
Thursday, November, 11, 2021
Friday, November, 12, 2021
Saturday, November, 13, 2021
Sunday, November, 14, 2021
Monday, November, 15, 2021
Tuesday, November, 16, 2021
Wednesday, November, 17, 2021
Thursday, November, 18, 2021
Friday, November, 19, 2021
Saturday, November, 20, 2021
Sunday, November, 21, 2021
Monday, November, 22, 2021
Tuesday, November, 23, 2021
Wednesday, November, 24, 2021
Thursday, November, 25, 2021
Friday, November, 26, 2021
Saturday, November, 27, 2021
Sunday, November, 28, 2021
Monday, November, 29, 2021
Tuesday, November, 30, 2021
How to display above dates in a template?
Simply place all dates in a list and pass the list to the context of the template.
year = today.year
month= today.month
num_days = calendar.monthrange(year, month)[1]
days = [datetime.date(year, month, day) for day in range(1, num_days+1)]
days_list = []
for days in days:
days_str = days.strftime('%A, %B, %d, %Y')
days_list.append(days_str)
context = {'days_list':days_list}
return render(request, 'template.html', context)
Then in your template
{% for day in days_list %}
{{day}}
{% endfor %}
Alternatively, you can pass the dates as datetime objects to the template and format them there using the Django built-in date filter.
I have a model database in this format
sno Date Premium Count Time Date_time Close
None Oct. 13, 2021 None 2 2:22 p.m. 38735
None Oct. 13, 2021 None 2 2:23 p.m. 38727
None Oct. 13, 2021 None 2 2:24 p.m. 38739
None Oct. 13, 2021 None 2 2:25 p.m. 38750
None Oct. 13, 2021 None 2 2:26 p.m. 38730
None Oct. 13, 2021 None 2 2:27 p.m. 38723
I want to make a simple bar chart of 'Close' column in the database. I have tried using some manual random values in chart.js code which I have done successfully, but how do I take the database field 'Close' , converting it into a list and plot a chart from it?
I have a table that looks like this:
Date Value
Oct. 23, 2018 -400
Oct. 23, 2018 -1100
Oct. 23, 2018 -200
Oct. 22, 2018 -400
Oct. 22, 2018 -1100
Oct. 21, 2018 -400
I would like to return the latest value for the date, but with multiple results.
filter().latest() only returns one object. I'd need three in this case.
Thanks!
You can give a try:
filter('some_filter_conditions').order_by('-date')[:3]
I am trying yo learn quantlib (1.3) & python bindings using quantlib-swig (1.2) in ubuntu 13.04. As a starter I am trying to determine the payment dates for a very simple bond as given below using 30/360 European day counter
from QuantLib import *
faceValue = 100.0
doi = Date(31, August, 2000)
dom = Date(31, August, 2008)
coupons = [0.05]
dayCounter = Thirty360(Thirty360.European)
schedule = Schedule(doi, dom, Period(Semiannual),
India(),
Unadjusted, Unadjusted,
DateGeneration.Backward, False)
Following are my questions:
Which method of schedule object will give me the payment dates?
Where do I need to specify the dayCounter object so that the dates are appropriately calculated?
Using Dimitri Reiswich' Presentation, I tried mimicking C++ code, but schedule.dates() returns an error as no such method.
The payment dates for this Fixed Rate bond are, (obtained by using oocalc)
Feb 28, 2001; Aug 31, 2001
Feb 28, 2002; Aug 31, 2002
Feb 28, 2003; Aug 31, 2003
Feb 29, 2004; Aug 31, 2004
Feb 28, 2005; Aug 31, 2005
Feb 28, 2006; Aug 31, 2006
Feb 28, 2007; Aug 31, 2007
Feb 29, 2008; Aug 31, 2008
How do I get the payment dates for this simple bond using python & quantlib? Can someone please help?
regards
K
If you want to look at the schedule you just generated, you can iterate over it:
>>> for d in schedule: print d
...
August 31st, 2000
February 28th, 2001
August 31st, 2001
February 28th, 2002
August 31st, 2002
February 28th, 2003
August 31st, 2003
February 29th, 2004
August 31st, 2004
February 28th, 2005
August 31st, 2005
February 28th, 2006
August 31st, 2006
February 28th, 2007
August 31st, 2007
February 29th, 2008
August 31st, 2008
or call list(schedule) if you want to store them. However, are you sure that those are the payment dates? They are the start and end date for accrual calculation; but some of these fall on a Saturday or a Sunday, and the bond will be paying on the next business day. You can see the effect if you instantiate the bond and retrieve the coupons:
>>> settlement_days = 3
>>> bond = FixedRateBond(settlement_days, faceValue, schedule, coupons, dayCounter)
>>> for c in bond.cashflows():
... print c.date()
...
February 28th, 2001
August 31st, 2001
February 28th, 2002
September 2nd, 2002
February 28th, 2003
September 1st, 2003
March 1st, 2004
August 31st, 2004
February 28th, 2005
August 31st, 2005
February 28th, 2006
August 31st, 2006
February 28th, 2007
August 31st, 2007
February 29th, 2008
September 1st, 2008
September 1st, 2008
(that is, unless Saturdays and Sundays shouldn't be holidays for the Indian calendar. If you think they shouldn't, file a bug report with QuantLib).
I asked this question before and got a reply that solved it for me. I have a dataframe that looks like this:
id weekdays halflife
241732222300860000 Friday, Aug 31, 2012, 22 0.4166666667
241689170123309000 Friday, Aug 31, 2012, 19 0.3833333333
241686878137512000 Friday, Aug 31, 2012, 19 0.4
241651117396738000 Friday, Aug 31, 2012, 16 1.5666666667
241635163505820000 Friday, Aug 31, 2012, 15 0.95
241633401382265000 Friday, Aug 31, 2012, 15 2.3666666667
And I would like to get average half life of items that were created on Monday, then on Tuesday...etc. (My date range spans over 6 months).
To get the date values I used strptime and difftime. Also, I found the maximum halflife with max(df$halflife), how can I find which id it corresponds to?
Reproducible code:
structure(list(id = c(241732222300860416, 241689170123309056,
241686878137511936, 241651117396738048, 241635163505819648, 241633401382264832
), weekdays = c("Friday, Aug 31, 2012, 22", "Friday, Aug 31, 2012, 19",
"Friday, Aug 31, 2012, 19", "Friday, Aug 31, 2012, 16", "Friday, Aug 31, 2012, 15",
"Friday, Aug 31, 2012, 15"), halflife = structure(c(0.416666666666667,
0.383333333333333, 0.4, 1.56666666666667, 0.95, 2.36666666666667
), class = "difftime", units = "mins")), .Names = c("id",
"weekdays", "halflife"), row.names = c(NA, 6L), class = "data.frame")
So now, I have an average half life value for all mondays, tuesdays...etc. How can I get the average value for all hours within those weekdays, i.e.: Average half life of all items that were created on all Mondays at 9am, then 10am, then 11am..etc. And then Tuesday at 9am, 10am, 11am..etc. The dates in the weekdays column is formatted so that the last number after the comma is the hour it was created at. I am really bad with regular expressions and pattern matching, which is why I am asking this follow-up question.
with base packages you can do following.
> mydf
id weekdays halflife
1 2.417322e+17 Friday, Aug 31, 2012, 22 0.4166667 mins
2 2.416892e+17 Friday, Aug 31, 2012, 19 0.3833333 mins
3 2.416869e+17 Friday, Aug 31, 2012, 19 0.4000000 mins
4 2.416511e+17 Friday, Aug 31, 2012, 16 1.5666667 mins
5 2.416352e+17 Friday, Aug 31, 2012, 15 0.9500000 mins
6 2.416334e+17 Friday, Aug 31, 2012, 15 2.3666667 mins
Instead of using regex, we can just use strsplit on each element of weekdays, unlist the result, and it back in 4 column format as matrix and cbind it back with mydf.
> mydf2 <- cbind(mydf, matrix(unlist(sapply(mydf$weekdays, strsplit, split=',')), byrow=TRUE, ncol=4, dimnames=list(1:nrow(mydf), c('Weekday', 'Day', 'Year', 'Hour'))))
> mydf2
id weekdays halflife Weekday Day Year Hour
1 2.417322e+17 Friday, Aug 31, 2012, 22 0.4166667 mins Friday Aug 31 2012 22
2 2.416892e+17 Friday, Aug 31, 2012, 19 0.3833333 mins Friday Aug 31 2012 19
3 2.416869e+17 Friday, Aug 31, 2012, 19 0.4000000 mins Friday Aug 31 2012 19
4 2.416511e+17 Friday, Aug 31, 2012, 16 1.5666667 mins Friday Aug 31 2012 16
5 2.416352e+17 Friday, Aug 31, 2012, 15 0.9500000 mins Friday Aug 31 2012 15
6 2.416334e+17 Friday, Aug 31, 2012, 15 2.3666667 mins Friday Aug 31 2012 15
Now we have split weekdays column appropriately, we can use aggregate function to calculate mean over desired grouping columns.
> aggregate(halflife ~ Weekday, data=mydf2, FUN = mean)
Weekday halflife
1 Friday 1.013889
If you want to group by Weekday as well as Hour then
> aggregate(halflife ~ Weekday + Hour, data=mydf2, FUN = mean)
Weekday Hour halflife
1 Friday 15 1.6583333
2 Friday 16 1.5666667
3 Friday 19 0.3916667
4 Friday 22 0.4166667
As such first parameter of aggregate function here is a forumla object which supports one ~ one, one ~ many, many ~ one, and many ~ many relationships. See ?aggregate examples to understand how to use it.
I will give brief example of how to many to many relationships.
> set.seed(12345)
> mydf2 <- cbind(mydf2, newvar = rnorm(nrow(mydf2)))
> mydf2
id weekdays halflife Weekday Day Year Hour newvar
1 2.417322e+17 Friday, Aug 31, 2012, 22 0.4166667 mins Friday Aug 31 2012 22 0.5855288
2 2.416892e+17 Friday, Aug 31, 2012, 19 0.3833333 mins Friday Aug 31 2012 19 0.7094660
3 2.416869e+17 Friday, Aug 31, 2012, 19 0.4000000 mins Friday Aug 31 2012 19 -0.1093033
4 2.416511e+17 Friday, Aug 31, 2012, 16 1.5666667 mins Friday Aug 31 2012 16 -0.4534972
5 2.416352e+17 Friday, Aug 31, 2012, 15 0.9500000 mins Friday Aug 31 2012 15 0.6058875
6 2.416334e+17 Friday, Aug 31, 2012, 15 2.3666667 mins Friday Aug 31 2012 15 -1.8179560
> aggregate(cbind(newvar,halflife) ~ Weekday + Hour, data=mydf2, FUN = mean)
Weekday Hour newvar halflife
1 Friday 15 -0.6060343 1.6583333
2 Friday 16 -0.4534972 1.5666667
3 Friday 19 0.3000814 0.3916667
4 Friday 22 0.5855288 0.4166667