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Filter one column and count values in other


Filter one column and count values in other

By : user2185085
Date : November 20 2020, 04:01 AM
I think the issue was by ths following , I have a database where is in column C the names of tools like: KDX15, GUA10, PKN01 and they are repeated in this column many times but they have added value each time in column D. , Use a Pivot Table …
… to get the following result:
code :


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Filter rows by count of two column values

Filter rows by count of two column values


By : maryam ahmed
Date : March 29 2020, 07:55 AM
it helps some times You can just add WHERE status = 'E0' inside your subquery:
SQL Fiddle (credit to Raging Bull for the fiddle)
code :
SELECT *
FROM (
    SELECT *,
        COUNT(MembershipNumber) OVER(PARTITION BY EmbossName) AS cnt
    FROM card
    WHERE status = 'E0'
)A
WHERE cnt > 1 
XSL filter one column and then count values in another column

XSL filter one column and then count values in another column


By : Jin Soo Hwang
Date : March 29 2020, 07:55 AM
To fix the issue you can do I have a SharePoint list with job information. I am trying to filter a column called ORGANISATION for the value Infrastructure. There is another column in the list called JOB STATUS which has values of Open, Offer or Filled. So i firstly filter for all the Infrastructure records, then i need to count the number of open roles, offered roles and filled roles for it. , Instead of for-each just use
code :
 <xsl:value-of select="count(/dsQueryResponse/Rows/Row[(normalize-space(@Title)='Infrastructure') and (normalize-space(@JobStatus)='OPEN')])"/>
filter column by count of distinct values

filter column by count of distinct values


By : Reva Malhotra
Date : March 29 2020, 07:55 AM
may help you . You can add another column in the summarize to count the number of records per group and then filter based on it:
code :
my_tibble %>% 
    group_by(A) %>% 
    summarise(percentage = mean(B), n = n()) %>% 
    filter(percentage > 0, n > 1)

# A tibble: 2 x 3
#      A percentage     n
#  <chr>      <dbl> <int>
#1     a       0.75     4
#2     b       0.50     2
Filter rows of a dataframe based on a column when both column name and values to filter are in variables

Filter rows of a dataframe based on a column when both column name and values to filter are in variables


By : jacky chan
Date : November 26 2020, 06:23 AM
Hope that helps For the following scenario I get an empty data frame , We can convert to symbol and evaluate (!!)
code :
dplyr::filter(D, !! rlang::sym(col) %in% vals)
#    X
#1 x1
#2 x2
D %>%
    filter_at(vars(col), any_vars(. %in% vals))
How can I filter time in a Dataframe column to output rows and count the different values on the other row?

How can I filter time in a Dataframe column to output rows and count the different values on the other row?


By : Lewy
Date : March 29 2020, 07:55 AM
Hope this helps I have a dataframe with two columns, Time and State. To create the time, I split a date and time column to create a new column as shown below. The new time column is recorded in HH:MM:SS format. , I edited my answer based on some comments and the new sample data:
code :
d = {'State':['Reply', 'Reply', 'Forward', 'Forward', 'Reply'], 'time': ['04:01:00', '08:04:00', '09:05:00', '12:06:00', '13:07:00']}
data = pd.DataFrame(data=d)

>          State      time
>     0    Reply  04:01:00
>     1    Reply  08:04:00
>     2  Forward  09:05:00
>     3  Forward  12:06:00
>     4    Reply  13:07:00
data.index = pd.to_datetime(data['time'])

>                            State      time
>     time                                  
>     2019-10-19 04:01:00    Reply  04:01:00
>     2019-10-19 08:04:00    Reply  08:04:00
>     2019-10-19 09:05:00  Forward  09:05:00
>     2019-10-19 12:06:00  Forward  12:06:00
>     2019-10-19 13:07:00    Reply  13:07:00
data['State'].between_time('09:00:00', '13:00:00').value_counts()

> Forward    2
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