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Filtering with a only one conditional


Filtering with a only one conditional

By : user2174254
Date : October 18 2020, 08:10 PM
this will help I'm trying to write a code that uses only lambdas, filter, map and reduce (its a riddle) that accepts a tuple of integers and a tuple of functions, and returns a new tuple of integers who only return one true from the list of functions: , Here it is:
code :
a = lambda func, data: tuple(filter(lambda d: (sum(map(lambda f: f(d), func)) == 1), data))
func = (lambda x: x > 3, lambda x: x % 2 == 0)
data = (1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
a = lambda func, data: tuple(filter(lambda d: (sum(map(lambda f: f(d), func)) == 1), data))
print(a(func, data))

(2, 5, 7, 9)


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Conditional filtering using CONTAINS in sql

Conditional filtering using CONTAINS in sql


By : mary bender
Date : March 29 2020, 07:55 AM
I wish this help you I have a parameter called @Param that can be null or not null. , Something like this probably:
code :
select * from myTable
where @param is null or contains(MyColumn, @Param)
filtering ng-repeat with more than one conditional

filtering ng-repeat with more than one conditional


By : user5389988
Date : March 29 2020, 07:55 AM
wish of those help From the Angular documentation
code :
<div id="box" ng-repeat="item in produtos | filter:categoria:busca.categoria | filter:subcategoria:busca.subcat | filter:produto:termoBusca>
SQL Conditional Filtering On Int Column

SQL Conditional Filtering On Int Column


By : user1850804
Date : March 29 2020, 07:55 AM
like below fixes the issue You don't need a CASE statement to do this, you can do it directly in the WHERE clause, like so:
code :
WHERE 
(
  --OTHER FILTERS HERE
)
AND
(
    -- if @profanity filter is true then we want all with counter greater than 0
    (@Profanity = '1' AND R.ProfanitiesFound > '0')
    OR
    -- else if @profanty filter is false then we want all with counter equal to 0
    (@Profanity = '0' AND R.ProfanitiesFound = '0')
    OR
    -- if nothing was sent in then just show all
    ((@Profanity IS NULL OR @Profanity = '') AND R.ProfanitiesFound = R.Profanities)
)
Conditional value filtering

Conditional value filtering


By : Supadi's Slow Aja
Date : March 29 2020, 07:55 AM
it fixes the issue Sample data
code :
library(dplyr)

df <- data.frame(
  ID = c(1,1,1,1,2,2,2,3,3,3),
  day = c(3,8,14,29,4,6,8,1,4,9),
  value = c(75, 101, 115, 120, 110, 106, 122, 100, 128, 140))

df %>%
  group_by(ID) %>%
  mutate(v = value[day == min(day)] * 1.3,
         flag = value > v) %>%
  ungroup()

# # A tibble: 10 x 5
#      ID   day value     v flag 
#   <dbl> <dbl> <dbl> <dbl> <lgl>
# 1     1     3    75  97.5 FALSE
# 2     1     8   101  97.5 TRUE 
# 3     1    14   115  97.5 TRUE 
# 4     1    29   120  97.5 TRUE 
# 5     2     4   110 143   FALSE
# 6     2     6   106 143   FALSE
# 7     2     8   122 143   FALSE
# 8     3     1   100 130   FALSE
# 9     3     4   128 130   FALSE
#10     3     9   140 130   TRUE 
 df %>%
  group_by(ID) %>%
  mutate(v = value[day == min(day)] * 1.3,
         flag = value > v,
         flag2 = max(flag)) %>%
  ungroup()

# # A tibble: 10 x 6
#      ID   day value     v flag  flag2
#   <dbl> <dbl> <dbl> <dbl> <lgl> <int>
# 1     1     3    75  97.5 FALSE     1
# 2     1     8   101  97.5 TRUE      1
# 3     1    14   115  97.5 TRUE      1
# 4     1    29   120  97.5 TRUE      1
# 5     2     4   110 143   FALSE     0
# 6     2     6   106 143   FALSE     0
# 7     2     8   122 143   FALSE     0
# 8     3     1   100 130   FALSE     1
# 9     3     4   128 130   FALSE     1
#10     3     9   140 130   TRUE      1
df %>%
  group_by(ID) %>%
  mutate(v = value[day == min(day)] * 1.3,
         flag = value > v) %>%
  ungroup() -> df2

df2 %>% 
  filter(flag == TRUE) %>%
  distinct(ID) %>%
  pull(ID)

#[1] 1 3
Conditional Filtering using R

Conditional Filtering using R


By : Brian
Date : September 30 2020, 09:00 PM
will be helpful for those in need Consider the below given dataframe; , Here is a base R approach:
code :
name <- df[df$Name == "D", "Name"]
ifelse(identical(name, character(0)), NA, name)

[1] NA
df <- data.frame(Name=c("EF", "GH", "VB"), 
                Age=c(50, 60, 70), 
                Type=c("A", "B", "C"),
                stringsAsFactors=FALSE)
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