Values are for two groups by quarter.
In DAX, need to summarize all the data but also need to remove -3 from each quarter in 2021 for Group 1, without allowing the value to go below 0.
This only impacts:
Group 1 Only
2021 Only
However, I also need to retain the data details without the adjustment. So I can't do this in Power Query. My data detail is actually in months but I'm only listing one date per quarter for brevity.
Data:
Group
Date
Value
1
01/01/2020
10
1
04/01/2020
8
1
07/01/2020
18
1
10/01/2020
2
1
01/01/2021
12
1
04/01/2021
3
1
07/01/2021
7
1
10/01/2021
2
2
01/01/2020
10
2
04/01/2020
8
2
07/01/2020
18
2
10/01/2020
2
2
01/01/2021
12
2
04/01/2021
3
2
07/01/2021
7
2
10/01/2021
2
Result:
Group
Qtr/Year
Value
1
Q1-2020
10
1
Q2-2020
8
1
Q3-2020
18
1
Q4-2020
2
1
2020
38
1
Q1-2021
9
1
Q2-2021
0
1
Q3-2021
4
1
Q4-2021
0
1
2021
13
2
Q1-2020
10
2
Q2-2020
8
2
Q3-2020
18
2
Q4-2020
2
2
2020
2
2
Q1-2021
12
2
Q2-2021
3
2
Q3-2021
7
2
Q4-2021
2
2
2021
24
You issue can be solved by using Matrix Table, and also to add new column to process value before create the table:
First, add a new column using following formula:
Revised value =
var newValue = IF(YEAR(Sheet1[Date])=2021,Sheet1[Value]-3,Sheet1[Value])
return
IF(newValue <0,0,newValue)
Second, create the matrix table for the desired outcome:
Related
I have a dataset on multiple outcome for individuals in two groups that were treated (or not treated) by an intervention at two time points. However, not every individual has complete data for each measure at each time point.
id
outcome
outcome_value
group
time
1
depression
10
1
1
1
depression
8
1
2
2
depression
10
2
1
2
depression
.
2
2
1
anxiety
12
1
1
1
anxiety
8
1
2
2
anxiety
12
2
1
2
anxiety
6
2
2
How do I exclude IDs that do not have an outcome in both periods? I only want to see how outcomes changed between groups over time for observations have data in all periods. I am using the mixed command in Stata to conduct this analysis.
First drop the missing rows
keep if !missing(outcome_value)
Then, keep the ID/outcome combinations that have _N==2
bysort id outcome: keep if _N==2
Output:
id outcome outco~ue group time ct
1 anxiety 8 1 2 2
1 anxiety 12 1 1 2
1 depression 10 1 1 2
1 depression 8 1 2 2
2 anxiety 6 2 2 2
2 anxiety 12 2 1 2
As #NickCox has pointed out in the comments, while we cannot directly combine these two, there is still a one-line approach:
bysort id outcome (time) : keep if !missing(outcome_value[1], outcome_value[2])
Of note, we cannot do this:
bysort id outcome : keep if !missing(outcome_value) & _N==2
because _N is not reduced by group until after the rows with missing outcome have been removed.
I begin to use Power BI, and I don't know how to group lines.
I have this kind of data :
api user 01/07/21 02/07/21 03/07/21 ...
a 25 null 3 4
b 25 1 null 2
c 25 1 4 5
a 30 4 3 5
b 30 3 2 2
c 30 1 1 3
And I would like to have the sum of the values per user, not by api and user
user 01/07/21 02/07/21 03/07/21 ...
25 2 7 11
30 8 6 10
Do you know how to do it please ?
I created a table with your sample data (make sure your values are treated as numbers):
Then create a Matrix visual, with "user" in Rows and your desired columns in the Values section:
I have two tables:
Table A
id
name
month_1
month_2
month_3
month_4
month_5
month_6
1
John
3
0
1
0
null
null
2
Mary
6
1
2
1
1
2
3
Angelo
1
5
null
null
null
null
4
Diane
3
2
0
1
null
null
Table B
id
name
LastYearTotal
CurrentYearTotal
1
John
2
4
2
Mary
6
13
3
Angelo
9
6
4
Diane
9
6
And then tables A and B will be side by side but not in the same table. Like there will be a separator between A and B. But when I use a filter, both tables will reflect the filter. In addition, there will only be one scroll for both tables so they move at the same time.
Thanks.
I have below dataset.
Math Literature Biology date student
4 2 5 2019-08-25 A
4 5 4 2019-08-08 A
5 4 5 2019-08-23 A
5 5 5 2019-08-15 A
5 5 5 2019-07-19 A
5 5 5 2019-07-15 A
5 5 5 2019-07-03 A
5 5 5 2019-06-26 A
1 1 2 2019-06-18 A
2 3 3 2019-06-14 A
5 5 5 2019-05-01 A
2 1 3 2019-04-26 A
I need to develop a solution in powerbi so in output I have cumulative average per subject per month
For example
April May June July August
Math | 2 3.5 3 3.75 4
Literature | 1 3 3 3.75 3.83
Biology | 3 4 3.6 4.125 4.33
Can you help?
You can use a matrix visualization for this.
Create a month-year variable and use it in the columns.
Use Average of Math,Literature and Biology in values
Under the format pane --> Values --> Show on rows --> Select this
This should give the view you are looking for. You can edit the value headers to your requirement.
My objective is to add rows in pandas in order to replace missing data with previous data and resample dates at the same time. Example :
This is what I have :
date wins losses
2015-12-19 11 5
2015-12-20 17 8
2015-12-20 10 6
2015-12-21 15 1
2015-12-25 11 5
2015-12-26 6 10
2015-12-27 10 6
2015-12-28 4 12
2015-12-29 8 11
And this is what I want :
wins losses
date
2015-12-19 11.0 5.0
2015-12-20 10.0 6.0
2015-12-21 15.0 1.0
2015-12-22 15.0 1.0
2015-12-23 15.0 1.0
2015-12-24 15.0 1.0
2015-12-25 11.0 5.0
2015-12-26 6.0 10.0
2015-12-27 10.0 6.0
2015-12-28 4.0 12.0
2015-12-29 8.0 11.0
And this is my code :
resamp = df.set_index('date').resample('D', how='last', fill_method='ffill')
It works !
But I want to do the same thing with 22 million lines (pandas), with different dates, and different IDs..
This dataframe contains two productID (1 and 2). I want to do the same previous exercice and keep the time serie data of every productID..
createdAt productId popularity
2015-12-01 1 5
2015-12-02 1 8
2015-12-04 1 6
2015-12-07 1 9
2015-12-01 2 5
2015-12-03 2 10
2015-12-04 2 6
2015-12-07 2 12
2015-12-09 2 11
This is my code :
df['date'] = pd.to_datetime(df['createdAt'])
df.set_index('date').resample('D', how='last', fill_method='ffill')
This is what I have if I use the same code ! I don't want a groupby with my dates.
createdAt productId popularity
date
2015-12-01 2015-12-01 2 5
2015-12-02 2015-12-02 2 5
2015-12-03 2015-12-03 2 10
2015-12-04 2015-12-04 2 6
2015-12-05 2015-12-05 2 6
2015-12-06 2015-12-06 2 6
2015-12-07 2015-12-07 2 12
2015-12-08 2015-12-08 2 12
2015-12-09 2015-12-09 2 11
This is what I want !
createdAt productId popularity
2015-12-01 1 5
2015-12-02 1 8
2015-12-03 1 8
2015-12-04 1 6
2015-12-05 1 6
2015-12-06 1 6
2015-12-07 1 9
2015-12-01 2 5
2015-12-02 2 5
2015-12-03 2 10
2015-12-04 2 6
2015-12-05 2 6
2015-12-06 2 6
2015-12-07 2 12
2015-12-08 2 12
2015-12-09 2 11
What to do ?
Thank you
Try this, it should works :)
print df.set_index('date').groupby('productId', group_keys=False).apply(lambda
df: df.resample('D').ffill()).reset_index()
This produces what you said you wanted.
print df.groupby('productId', group_keys=False).apply(lambda df: df.resample('D').ffill()).reset_index()
createdAt productId popularity
0 2015-12-01 1 5
1 2015-12-02 1 8
2 2015-12-03 1 8
3 2015-12-04 1 6
4 2015-12-05 1 6
5 2015-12-06 1 6
6 2015-12-07 1 9
7 2015-12-01 2 5
8 2015-12-02 2 5
9 2015-12-03 2 10
10 2015-12-04 2 6
11 2015-12-05 2 6
12 2015-12-06 2 6
13 2015-12-07 2 12
14 2015-12-08 2 12
15 2015-12-09 2 11