I have a database in Influxdb 1.7 with several columns and no tag key.
I want to receive only fields where the value is greater than 0 (zero)
(I'm using grafana to show)
like:
table1:
| time | A | B | C | D | E | F |....| AD | AE | ...
|167344...| 0 | 0 | 2 | 0 | 12 | 0 |....| 0 | 0 |
|167356... | 0 | 0 | 2 | 0 | 0 | 0 |....| 0 | 12 |
|167376... | 0 | 0 | 2 | 0 | 1 | 0 |....| 0 | 0 |
|167384... | 1 | 4 | 0 | 0 | 0 | 0 |....| 0 | 1 |
I would like to receive:
| time | A | B | AE |
|167384... | 1 | 4 | 1 |
I've tried:
select last("*") from "table1" where * > 0
select last(*) from "table1" where * > 0
select last(*) as bbb from "table1" where bbb > 0
and other similar queries, but doen't work
Related
I have a Cypher query that shows the following output:
+----------------
| usid | count |
+----------------
| "000" | 1 |
| "000" | 0 |
| "000" | 0 |
| "001" | 1 |
| "001" | 1 |
| "001" | 0 |
| "002" | 2 |
| "002" | 2 |
| "002" | 0 |
| "003" | 4 |
| "003" | 2 |
| "003" | 2 |
| "004" | 4 |
| "004" | 4 |
| "004" | 4 |
+----------------
How can I get the below result with the condition SUM(count) <= 9.
+----------------
| usid | count |
+----------------
| "000" | 1 |
| "001" | 2 |
| "002" | 4 |
| "003" | 8 |
+----------------
Note: I have used the below query to get the 1st table data.
MATCH (us:USER)
WITH us
WHERE us.count <= 4
RETURN us.id as usid, us.count as count;
I don't know how you get your original data, so I will just use a WITH clause and assume the data is there:
// original data
WITH usid, count
// aggregate and filter
WITH usid, sum(count) as new_count
WHERE new_count <= 9
RETURN usid, new_count
Based on the updated question, the new query would look like:
MATCH (us:USER)
WHERE us.count <= 4
WITH us.id as usid, sum(us.count) as count
WHERE new_count <= 9
RETURN usid, count
˙˙˙
Suppose I have a simple sheet like
|| A | B | C | D | E | F | G
==============================
1 || 1 | 0 | | ? | 1 | 0 | 1
I want to create a new row where each value is either 1 or 0. The logic is if the cell is either blank or ? then it should be 0.
The closest I got was
=ArrayFormula(if(A1:G1="?", 0, A1:G1))
which gave me
|| A | B | C | D | E | F | G
==============================
1 || 1 | 0 | | ? | 1 | 0 | 1
2 || 1 | 0 | | 0 | 1 | 0 | 1
But as soon as I add an OR for checking blanks with
=ArrayFormula(if(or(A1:G1="?", isblank(A1:G1)), 0, A1:G1))
then I only get 1 cell:
|| A | B | C | D | E | F | G
==============================
1 || 1 | 0 | | ? | 1 | 0 | 1
2 || 1 | | | | | |
What am I doing wrong? Or is there a better way to do this?
Two ways:
Simply place the other condition in the value_is_false location
=ArrayFormula(if(A1:G1="?", 0, IF(ISBLANK(A1:G1), 0, A1:G1)))
Use + to represent OR. This works because "true" values are evaluated to 1 and "false" values are evaluated to 0. So, 0+0=false, 1+0=true. For AND, you multiply...0*0=false, 1*0=false, 1*1=true.
=ArrayFormula(if((A1:G1="?")+(ISBLANK(A1:G1)), 0, A1:G1))
How to join left next row in Cloud Spanner.
I want to calculate how many kilometers driven each driver.
My table looks like:
vehicle_id | driver_id | odometer
1 | 1 | 10
1 | 1 | 20
1 | 2 | 20
1 | 2 | 40
1 | 1 | 40
1 | 1 | 50
2 | 1 | 10
2 | 1 | 20
2 | 2 | 20
2 | 2 | 30
2 | 1 | 30
2 | 1 | 80
2 | 2 | 80
2 | 2 | 120
Results should be:
driver_id | total_mileage
1 | 80
2 | 70
My solution is:
SUM (mileage)
FROM (SELECT (odometer2-odometer) AS mileage
FROM (SELECT vehicle_id , odometer ,driver_id ,
NEXT.driver_id AS driver_id 2, NEXT.odometer AS odometer2 FROM Table
**JOIN NEXT ROW** AS NEXT
)
WHERE driver_id=driver_id2
)
GROUP BY driver_id
vehicle_id | driver_id | odometer | driver_id2 | odometer2 |mileage
1 | 1 | 10 | 1 | 20 | 10
1 | 1 | 20 | 2 | 20 | -
1 | 2 | 20 | 2 | 40 | 20
1 | 2 | 40 | 1 | 40 | -
1 | 1 | 40 | 1 | 50 | 10
1 | 1 | 50 | - | - | -
2 | 1 | 10 | 1 | 20 | 10
2 | 1 | 20 | 2 | 20 | -
2 | 2 | 20 | 2 | 30 | 10
2 | 2 | 30 | 1 | 30 | -
2 | 1 | 30 | 1 | 80 | 50
2 | 1 | 80 | 2 | 80 | -
2 | 2 | 80 | 2 | 120 | 40
2 | 2 | 120 | - | - | -
In Cloud Spanner function Row_number, OVER, LAG do not exist.
My question is how to join left next row in Cloud Spanner?
You're looking to PIVOT the data. This is not something that is doable directly in Cloud Spanner SQL - you'll need to post-process the data to affect the pivot.
I need help to interpret result in weka using the J48
I dont know how to explain the result, I am using the dataset Heart Disease Data Set from http://archive.ics.uci.edu/ml/datasets/Heart+Disease
And the J48 tree
Please help me, with some points importants for this analyse
my result is:
=== Run information ===
Scheme: weka.classifiers.trees.J48 -C 0.25 -M 2
Relation: AnaliseCardiaca
Instances: 303
Attributes: 14
age
sex
cp
trestbps
chol
fbs
restecg
thalach
exang
oldpeak
slope
ca
thal
num
Test mode: 10-fold cross-validation
=== Classifier model (full training set) ===
J48 pruned tree
cp <= 3
| sex <= 0: 0 (57.0/2.0)
| sex > 0
| | slope <= 1
| | | fbs <= 0
| | | | trestbps <= 152
| | | | | thalach <= 162
| | | | | | ca <= 1
| | | | | | | age <= 56: 0 (12.0/1.0)
| | | | | | | age > 56: 1 (3.0/1.0)
| | | | | | ca > 1: 1 (2.0)
| | | | | thalach > 162: 0 (27.0)
| | | | trestbps > 152: 1 (4.0/1.0)
| | | fbs > 0: 0 (9.0)
| | slope > 1
| | | slope <= 2
| | | | ca <= 0
| | | | | fbs <= 0
| | | | | | chol <= 261
| | | | | | | oldpeak <= 2.5: 0 (11.61/1.0)
| | | | | | | oldpeak > 2.5: 1 (3.0)
| | | | | | chol > 261: 1 (4.0)
| | | | | fbs > 0: 0 (4.0)
| | | | ca > 0
| | | | | thal <= 6: 1 (6.0/1.0)
| | | | | thal > 6
| | | | | | thalach <= 145: 0 (3.39)
| | | | | | thalach > 145: 1 (5.0/1.0)
| | | slope > 2: 0 (8.0/1.0)
cp > 3
| thal <= 3
| | ca <= 2
| | | exang <= 0
| | | | sex <= 0
| | | | | chol <= 304: 0 (14.0)
| | | | | chol > 304: 1 (3.0/1.0)
| | | | sex > 0
| | | | | ca <= 0: 0 (10.0/1.0)
| | | | | ca > 0: 1 (3.0)
| | | exang > 0
| | | | restecg <= 1
| | | | | slope <= 1: 0 (2.0)
| | | | | slope > 1: 1 (5.37)
| | | | restecg > 1
| | | | | ca <= 0: 0 (4.0)
| | | | | ca > 0
| | | | | | ca <= 1
| | | | | | | thalach <= 113: 0 (2.0)
| | | | | | | thalach > 113: 1 (4.0)
| | | | | | ca > 1: 0 (2.0)
| | ca > 2: 1 (4.0)
| thal > 3
| | fbs <= 0
| | | ca <= 0
| | | | chol <= 278: 0 (23.0/8.0)
| | | | chol > 278: 1 (6.0)
| | | ca > 0: 1 (46.0/12.0)
| | fbs > 0
| | | ca <= 1: 1 (3.88)
| | | ca > 1: 0 (11.75/4.75)
Number of Leaves : 31
Size of the tree : 61
Result img
If you are using Weka Explorer, you can right click on the result row in the results list (located on the left of the window under the start button). Then select visualize tree. This will display an image of the tree.
If you still want to understand the results as they are shown in your question:
The results are displayed as tree. The root of the tree starts at the left and the first feature used is called cp. If cp is smaller or equal to 3, then the next feature in the tree is sex and so on. You can see that when you split by sex and sex <= 0 you reach a prediction. The prediction is 0 and the (57/2) means that 57 observations in the training set end up at this path and 2 were incorrectly classified, i.e. 55 had the label 0 and 2 had the label 1.
Here is how the start of the tree looks like:
--------start---------
| |
| |
|cp > 3 | cp <= 3
_________|______ ____|__________
| | | |
|thal>3 |thal<=3 |sex>0 |sex<=0
| | | |
... ... ... prediction 0 57(55,2)
The AndreyF's explanation is good. I want to add some information.
Why does the tree have float numbers in its leaves? Can an instance (individual) be split and get a float value? (in the reality a person can not be split)
When the instance has all the attributes set perfectly then there isn't a problem. But when the instance has missing attributes, then the classifier (J48) doesn't know the way of the tree for that attribute.
For example, if an instance has its "oldpeak" attribute like a missing attribute then when it reaches the "chol <= 261" node (previous node to the "oldpeak" node) the classifier will divide the instance according to a probability and a percentage of the instance will go to "oldpeak <= 2.5" and the other percentage will go to "oldpeak > 2.5".
How does the classifier calculate that probability? It calculates through the instances that don't have the missing attribute for the actual node. For this example will be the "oldpeak" attribute.
If we have 25% instances with no missing "oldpeak" attribute that were classified in the "oldpeak <= 2.5" node, and we have 75% instances with no missing "oldpeak" attribute that were classified in the "oldpeak > 2.5" node then when the classifier wants to classify an instance with "oldpeak" attribute missing then the 25% of this instance will go through "oldpeak <= 2.5" and the rest (75%) will go through "oldpeak > 2.5".
You can try to remove instances with missing attributes and you will see that the tree will only have integer numbers instead of float numbers.
Thank you.
Can someone explain how these concept works?
I have 1 question. But I don't know have any ideas on constructing the truth table.
f(A,B,C) = AB + A’C
The answer given was ABC + ABC' + A'BC + A'B'C
And i have no idea how it get there. :-(
1. Create a column for each of the inputs, each intermediate functions, and the final function:
A B C | AB | A' | A'C | AB + A'C
--------------------------------
| | | |
| | | |
| | | |
| | | |
| | | |
| | | |
| | | |
| | | |
2. Enumerate all input possibilities, and start filling in the intermediate function values and then the final function value:
A B C | AB | A' | A'C | AB + A'C
--------------------------------
0 0 0 | 0 | 1 | 0 | 0
0 0 1 | | | |
0 1 0 | | | |
0 1 1 | | | |
1 0 0 | | | |
1 0 1 | | | |
1 1 0 | | | |
1 1 1 | | | |
3. Now, you finish the truth table.
Update per OP's edit of question:
The "answer given" can be reduced as follows using Boolean Algebra:
ABC + ABC' + A'BC + A'B'C
AB(C + C') + A'C(B + B')
AB + A'C
...which is the same as the given f(A,B,C). Not sure why ABC + ABC' + A'BC + A'B'C would be considered to be the "answer," but this does show equivalence between the two formulae.