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How to summarize data into table filtering by dates


How to summarize data into table filtering by dates

By : user3099623
Date : January 11 2021, 03:34 PM
I wish did fix the issue. Your filter expression should be using DATESBETWEEN and not the datediff:
code :
TestTable1 = 
    VAR LastEffDate = LASTDATE(fact_Premium[EffectiveDate])  // --  7/27/2019
RETURN 
    SUMMARIZE (
        FILTER (
             dim_Date, 
             DATESBETWEEN(
                 //expression for start date, 
                 //expression for end date
             )
        ),
        dim_Date[Year Month],
        "Premium", [Ttl WP]
    ) 


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Summarize grouped data in Pandas while filtering after the group part

Summarize grouped data in Pandas while filtering after the group part


By : munishK
Date : March 29 2020, 07:55 AM
To fix the issue you can do I am stumbling over the exact (or at least most elegant) steps to group and aggregate some data in Pandas. Let's say I have a DataFrame that looks something like this - , You can go by using the power of apply function:
code :
def conditional_sum(grp):
    grp['non_bar_total'] = grp[grp.datatype != 'bar']['datacount'].sum()
    return grp

df.groupby(['system', 'sub_system']).apply(conditional_sum)

system  sub_system  datatype    datacount   non_bar_total
0    a   1   foo     111     444
1    a   1   bar     222     444
2    a   1   baz     333     444
3    a   2   foo     444     999
4    a   2   baz     555     999
5    b   1   foo     667     667
6    b   3   baz     777     777
Issue with filtering data.table for dates - works with fixed date but not with variable

Issue with filtering data.table for dates - works with fixed date but not with variable


By : user3597368
Date : March 29 2020, 07:55 AM
wish help you to fix your issue This is due to the fact, that one of the columns of your data.table is named x. As data.table evaluates its first argument (the one you are using) it looks for x as column reference first. So in fact x>begin_idate is interpreted as
code :
dt$x > dt$begin_idate 
xx = as.Date('2014-01-09')
dt[xx>begin_idate  & xx<=end_idate ]
> dt[xx>begin_idate  & xx<=end_idate ]
   begin   end x begin_idate  end_idate
1: 16076 16080 J  2014-01-06 2014-01-10
2: 16077 16082 J  2014-01-07 2014-01-12
3: 16078 16084 J  2014-01-08 2014-01-14
all.equal(dt[as.Date('2014-01-09')>begin_idate  & as.Date('2014-01-09')<=end_idate ],
          dt[xx>begin_idate  & xx<=end_idate ]) # TRUE
R - Filtering character dates in data.table

R - Filtering character dates in data.table


By : analisifunzionale
Date : March 29 2020, 07:55 AM
fixed the issue. Will look into that further The default way of reading dates in data.table with fread is that dates are stored as character values. Using this default, I noticed that filtering in i for a date range using logical comparison versus %in% operator are dramatically different in execution time: , This is to be expected and is not related to data.table or dates:
code :
 myvec <- rep(c("111111","999999"),1e7)
 mycompvec <- as.character(111111:999999)

 system.time(myvec%in%mycompvec)
 #   user  system elapsed 
 #   1.39    0.08    1.49 
system.time(myvec<="999999"&myvec>="111111")
#    user  system elapsed 
#    9.92    0.03   10.03 
Summarize data.frame by dates in R

Summarize data.frame by dates in R


By : Simer
Date : March 29 2020, 07:55 AM
it should still fix some issue Reproducible input: , I would use data.table and do something like the following:
code :
library(data.table)
setDT(df)
ndf <- df[, 
          .(Date = paste(Date[1], "to", Date[.N]), weather = Type.of.Weather[1]),
          rleid(Type.of.Weather)
          ][,
            rleid := NULL
           ][]
ndf
               Date weather
1: 01-Jan to 03-Jan  Cloudy
2: 04-Jan to 10-Jan   Rainy
3: 11-Jan to 15-Jan  Cloudy
4: 16-Jan to 20-Jan   Sunny
Summarize data across dates

Summarize data across dates


By : pebuardi
Date : March 29 2020, 07:55 AM
hope this fix your issue Here's a not-too-pretty solution combining a dplyr call within another one. Or, you could separate the data into the start date values and end date values, then merge them.
code :
d %>% filter(date==end_date) %>% mutate(val.end=val) %>% 
  select(-val,-date) %>% 
  left_join(d %>% filter(date==start_date) %>% mutate(val.start=val) %>% 
      select(-val,-date),.) %>% mutate(diff=(val.end-val.start)/val.start)

# A tibble: 38 x 6
   ID       start_date end_date   val.start val.end    diff
   <chr>    <date>     <date>         <dbl>   <dbl>   <dbl>
 1 00036110 2005-04-30 2006-03-31      14.7    28.5  0.933 
 2 00036110 2006-04-30 2007-03-31      26.7    27.6  0.0330
 3 00036110 2007-04-30 2008-03-31      30.5    27.3 -0.107 
 4 00036110 2008-04-30 2009-03-31      23.4    12.5 -0.464 
 5 00036110 2008-04-30 2009-03-31      23.4    12.5 -0.464 
 6 00036110 2008-04-30 2009-03-31      23.4    12.5 -0.464 
 7 00036110 2008-04-30 2009-03-31      23.4    12.5 -0.464 
 8 00036110 2009-04-30 2010-03-31      15.1    24.8  0.647 
 9 00036110 2009-04-30 2010-03-31      15.1    24.8  0.647 
10 00036110 2009-04-30 2010-03-31      15.1    24.8  0.647 
# ... with 28 more rows
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