It seems very simple but I can not get the graph to show the data I want.
So, I have got a lot of IDs with the end and start dates (LENGHT) and open items (OPEN). Each day has got availability (AVAIL) and there is nil used (USED) at day 1.
ID LENGTH OPEN USED AVAIL
1A 6 100 0 2400
I need to create the NEW_DAY column with count of the LENGHT. In this case the result would be
ID LENGTH NEW_DAY OPEN USED AVAIL
1A 6 1 100 0 2400
1A 6 2 100 0 2400
1A 6 3 100 0 2400
1A 6 4 100 0 2400
1A 6 5 100 0 2400
1A 6 6 100 0 2400
Note, I have hundreds of IDs so can not hard code it as 1A and needs to be dynamic.
I am not sure, but maybe this might help you.
If you add a blank query and add this expression:
= List.Repeat({1, 2}, 3)
you will get the first argument {1, 2} repeated three times.
When you separate your ID in a new column and pass this column to the code above (the same goes for the second argument) it might work.
Related
I have a table with the columns as below -
There are rows showing the allocation of various people under various project. The months (columns) can extend to Dec,20 and continue on from Jan,21 in the same pattern as above.
One Staff can be tagged to any number of projects in a given month.
Now I want to prepare a Power BI report on this in the format as below -
Staff ID, Project ID and End Date are the slicers to be present.
For the End Date slicer we can select options in the format of (MMM,YY) (eg - Jan,23). On the basis of this slicer I want to show the preceding 6 months of data, as portrayed by the above sample image.
I have tried using parameters but those have to specified for each combination so not usable for this data as this is going to increase over time.
Is there any way to do this or am I missing some simple thing in particular?
Any help on this will be highly appreciated.
Adding in the sample data as below -
Staff ID
Project ID
Jan,20
Feb,20
Mar,20
Apr,20
May,20
Jun,20
Jul,20
1
20
0
0
0
100
80
10
0
1
30
0
0
0
0
20
90
100
2
20
100
100
100
0
0
0
0
3
50
80
100
0
0
0
0
0
3
60
15
0
0
0
20
0
0
3
70
5
0
100
100
80
0
0
I have a set of individuals with characteristics. Each individual belongs to one or more group. I need to merge individuals to group characteristics, by firstly duplicating each row of individual data set as many times as is given by n_groups.
The data looks like
id age n_groups
1 50 2
2 46 1
3 51 3
4 44 2
I need to have
id age n_groups group_index
1 50 2 1
1 50 2 2
2 46 1 1
3 51 3 1
3 51 3 2
3 51 3 3
4 44 2 1
4 44 2 1
It seems like a very easy task, and I need some variation of expand with variable number of duplicates. Any ideas if there is a simple command for this?
Thanks!
Appears the solution is very standard. The expand command indeed allows for expanding based on variable: expand n_groups solved the question.
I have a Calc sheet listing a cut-list for plywood in two columns with a quantity in a third column. I would like to remove duplicate matching pairs of dimensions and total the quantity. Starting with:
A B C
25 35 2
25 40 1
25 45 3
25 45 2
35 45 1
35 50 3
40 25 1
40 25 1
Ending with:
A B C
25 35 2
25 40 1
25 45 5
35 45 1
35 50 3
40 25 2
I'm trying to automate this. Currently I have multiple lists which occupy the same page which need to be totaled independently of each other.
Put a unique different ListId, ListCode or ListNumber for each of the lists. Let all rows falling into the same list, have the same value for this field.
Concatenate A & B and form a new column, say, PairAB.
If the list is small and handlable, filter for PairAB and collect totals.
Otherwise, use Grouping and subtotals to get totals for each list and each pair, grouping on ListId and PairAB.
If the list is very large, you are better off taking it to CSV, and onward to a database, such things are simple child's play in SQL.
I have a data frame that looks like this:
id age sallary
1 16 500
2 21 1000
3 25 3000
4 30 6000
5 40 25000
and a list of ids that I would like to ignore [1,3,5]
how can I get a data frame that will contain all the remaining rows: 2,4.
Big thanks for every one.
Call isin and negate the result using ~:
In [42]:
ignore_ids=[1,3,5]
df[~df.id.isin(ignore_ids)]
Out[42]:
id age sallary
1 2 21 1000
3 4 30 6000
Imagine a number guessing game where one person thinks of a number and another person has to guess it. The game is over if the correct number was guessed.
The models might look like this
class SecretNumber(models.Model):
number = models.IntegerField()
class Guess(models.Model)
secretnumber = models.Foreignkey(SecretNumber)
guess = models.IntegerField()
After having played four times, the database might look like this:
id number
==========
1 10
2 54
3 68
4 25
id secretnumber_id guess
=============================
1 1 50
2 1 30
3 1 10
4 2 99
5 2 60
6 2 54
7 3 1
8 3 68
9 4 73
10 4 34
11 4 86
12 4 51
13 4 25
As you can see, the guesser was very lucky: it took him 3, 3, 2 and 4 guesses. But that's just to keep this example short.
Now I need to come up with a query which will allow to display the following data:
Nb. guesses Count
=====================
2 1
3 2
4 1
A manual SQL statement would look something like this:
SELECT inner_count AS 'Nb. guesses', count(inner_count) AS 'Count' FROM (
SELECT secretnumber_id, count(id) AS inner_count FROM guess GROUP BY secretnumber_id
) GROUP BY inner_count
I thought about annotating an annotation, but this seems not to be possible.
Any ideas?
If you're using django (ie models instead of classes), you want to use the QuerySet aggregate functions
e.g.
from django.db.models import Count
guesses = Guess.objects.values('secretnumber').annotate(Count('secretnumber'))
This will give you a queryset with a list of objects, which have a secretnumber and a count value.