I have a list of time series and I have extracted the time and date field for my calculation. I would Like to insert all the missing dates that fall under two row,Like the one in the screenshotenter image description here.
P.S. I do not have a code to add here.
Update : I have tried to add a lag column to get the next time and then a java script to find the number of interval. Now I have a number of columns to be inserted but I am finding it difficult to insert the rows and also is there any other efficient way than this?
Update 2:
I have tried generating time series like
Date and time Group
2012-02-24 0
2012-02-24 1
2012-02-24 2
2012-02-24 3
2012-02-25 0
2012-02-25 1
2012-02-25 2
2012-02-25 3
And I have a time series like
Date and time Group
24.2.2012 1
24.2.2012 2
24.2.2012 3
25.2.2012 0
25.2.2012 1
25.2.2012 2
25.2.2012 3
May i know how to merge them in knime to achieve
Date and time Group
2012-02-24 Null
2012-02-24 1
2012-02-24 2
2012-02-24 3
2012-02-25 0
2012-02-25 1
2012-02-25 2
2012-02-25 3
I was able to produce it by creating a unique date series and then using Join node and then sorting it based on Date. Thank you.
Related
Hi everybody I am trying to query an already formatted google sheets, I am able to filter some of those data (I used =query(x,select * where ... )). The output I get is the following:
may
may
june
june
july
july
july
planned
name
1
0
1
1
2
3
1
Now I want to refer to all the numbers under may (or june or july) in order to do some operation. I can' t just select the value I want because I need to automate it.
How can I get all the columns containing a specific marker(in my case the name of the month)? If it is not possible can you suggest me a different way to do that ? (I am not very experienced with google sheets or excel)
Since query can't select rows, you'd transpose it first and then select the columns you want and then retranspose it back, if needed:
Input:
may
may
june
june
july
july
july
planned
name
1
0
1
1
2
3
1
Formula(select columns >0):
=QUERY(TRANSPOSE(A27:I28),"Select * where Col2>0")
Output:
planned name
may
1
june
1
june
1
july
2
july
3
july
1
I have a problem. I want to predict when the customer will place another order in how many days if an order comes in.
I have already created my target variable next_purchase_in_days. This specifies in how many days the customer will place an order again. And I would like to predict this.
Since I have too few features, I want to do feature engineering. I would like to specify how many orders the customer has placed in the last 90 days. For example, I have calculated back from today's date how many orders the customer has placed in the last 90 days.
Is it better to say per row how many orders the customer has placed? Please see below for the example.
So does it make more sense to calculate this from today's date and include it as a feature or should it be recalculated for each row?
customerId fromDate next_purchase_in_days
0 1 2021-02-22 24
1 1 2021-03-18 4
2 1 2021-03-22 109
3 1 2021-02-10 12
4 1 2021-09-07 133
8 3 2022-05-17 61
10 3 2021-02-22 133
11 3 2021-02-22 133
Example
# What I have
customerId fromDate next_purchase_in_days purchase_in_last_90_days
0 1 2021-02-22 24 0
1 1 2021-03-18 4 0
2 1 2021-03-22 109 0
3 1 2021-02-10 12 0
4 1 2021-09-07 133 0
8 3 2022-05-17 61 1
10 3 2021-02-22 133 1
11 3 2021-02-22 133 1
# Or does this make more sense?
customerId fromDate next_purchase_in_days purchase_in_last_90_days
0 1 2021-02-22 24 1
1 1 2021-03-18 4 2
2 1 2021-03-22 109 3
3 1 2021-02-10 12 0
4 1 2021-09-07 133 0
8 3 2022-05-17 61 1
10 3 2021-02-22 133 0
11 3 2021-02-22 133 0
You can address this in a number of ways, but something interesting to consider is the interaction between Date & Customer ID.
Dates have meaning to humans beyond just time keeping. They are associated with emotional, and culturally importance. Holidays, weekends, seasons, anniversaries etc. So there is a conditional relationship between the probability of a purchase and Events: P(x|E)
Customer Ids theoretically represent a single person, or at the very least a single business with a limited number of people responsible for purchasing.
Certain people/corporations are just more likely to spend.
So here are a number of ways to address this:
Find a list of holidays relevant to the users. For instance if they are US based find a list of US recognized holidays. Then create a
feature based on each date: Date_Till_Next_Holiday or (DTNH for
short).
Dates also have cyclical aspects that can encode probability. Day of the > year (1-365), Days of the week (1-7), week numbers (1-52),
Months (1-12), Quarters (1-4). I would create additional columns
encoding each of these.
To address the customer interaction, have a running total of past purchases. You could call it Purchases_to_date, and would be an
integer (0...n) where n is the number of previous purchases.
I made a notebook to show you how to do running totals.
Humans tend to share purchasing patterns with other humans. You could run a k-means cluster algorithm that splits customers into 3-4
groups based on all the previous info, and then use their
cluster-number as a feature. Sklearn-Kmeans
So based on all that you could engineer 8 different columns. I would then run Principle Component Analysis (PCA) to reduce that to 3-4 features.
You can use Sklearn-PCA to do PCA.
I'm trying to build a sheet where I can see how much I have to pay each month.
Let's say I have the following table
Current installment (CI)
Total installments (TI)
Installment amount (IA)
1
3
$100
1
1
$200
2
3
$150
1
3
$75
2
4
$150
1
1
$50
So, the first month would be if TI-CI >= 1, then I will sum that value. For the following month I would do the same but TI-CI >= 2
And the result would be something like this
-
-
1st month debt
$475 (the result of 100+150+75+100)
2nd month debt
$325 (the result of 100+75+150)
3rd month debt
$100
Is this possible at all?
try:
=IFNA(SUM(FILTER(C$2:C, (B$2:B-A$2:A)>=ROW(A1))))
and drag down
I have a massive dataset and am preparing a dashboard based on this dataset.
On my dashboard, I have a drop-down menu that allows me to select a month of my choice, from Jan to Apr.
Visitor Jan Feb Mar Apr
Jenny 2 3 0 1
Peter 2 0 1 3
Charley 0 2 4
Charley 1 2 2 3
Sam 1 4 2 3
Peter 2 2 5 0
John 3 3 6 9
Robin 4 0 7 0
I am looking for a formula that will give me the number of unique visitors who have been active at least once in the month that I choose from the drop-down menu.
Hoping this is really clear, but if not, please feel free to shoot back your questions.
This may be easier with Excel 2013, but if the results you want from your example are 6, 5, 5, and 5 for Jan>April respectively then perhaps:
Create a PivotTable from multiple consolidation ranges (example how here and for VALUES choose Sum of Value.
Count the non-zero values in the PT by column with a formula such as:
=COUNTIF(H5:H10,">"&0)
The above however would not be convenient for repetition each month, though a whole year might be prepared at one time.
I'm using SPSS and have a dataset comprised of individuals' responses to a survey question. This is longitudinal data, so the subjects have taken the survey at least twice and some as many as four or five times.
My variables are ID (scale), date of survey completion (date - dd-mmm-yyyy), and response to survey question (scale).
The dataset is sorted by ID then date (ascending). Each date corresponds to survey time 1, time 2, etc. What I would like to do is compute a new variable time that corresponds to the survey completion dates for a particular participant. I would then like to use that variable to complete a long-to-wide restructuring of the dataset.
So, I'd like to accomplish the following and am not sure how to go about doing it:
1) I have something like this:
ID Date Assessment_Answer
----------------------------------
1 01-Jan-2009 4
1 01-Jan-2010 1
1 01-Jan-2011 5
2 15-Oct-2012 6
2 15-Oct-2012 0
2) Want to compute another variable that would give me this:
ID Date Assessment_Answer Time
-----------------------------------------
1 01-Jan-2009 4 Time1
1 01-Jan-2010 1 Time2
1 01-Jan-2011 5 Time3
2 15-Oct-2012 6 Time1
2 15-Oct-2013 0 Time2
3) And restructure so that I have something like this:
ID Time1 Time2 Time3 Time4
--------------------------
1 4 1 5
2 6 0
You can use sequential case processing to create a variable that is a counter within each ID. So for example:
*Making fake data.
DATA LIST FREE / ID (F1.0) Date (DATE10) Assessment_Answer (F1.0).
BEGIN DATA
1 01-Jan-2009 4
1 01-Jan-2010 1
1 01-Jan-2011 5
2 15-Oct-2012 6
2 15-Oct-2012 0
END DATA.
*Making counter within ID.
SORT CASES BY Id Date.
DO IF ($casenum = 1) OR (Id <> LAG(ID)).
COMPUTE Time = 1.
ELSE.
COMPUTE Time = LAG(Time) + 1.
END IF.
FORMATS Time (F2.0).
EXECUTE.
Now you can use CASESTOVARS to reshape the data like you requested.
CASESTOVARS
/ID = Id
/INDEX = Time
/DROP Date.