I am scheduling games for a large basketball camp. The master spreadsheet will looks as follows:
TIME GYM TEAM A TEAM B
9:00 MCLELLAN 1 3
9:00 PRACTICE 2 4
9:00 MCKENZIE 5 6
9:00 ABC SCHOOL 6 1
9:00 HOME GYM 2 3
10:00 XYA SCHOOL 4 5
11:00 MCLLELLAN 1 2
12:00 PRACTICE 3 4
1:00 PRACTICE 5 6
After completing the master schedule though, I want to be able to automatically extract each team's games independently. For example, something like this for Team 1:
TIME GYM TEAM A TEAM B
9:00 MCLELLAN 1 3
9:00 ABC SCHOOL 6 1
11:00 MCLLELLAN 1 2
There will be approximately 40 teams and about 200 games total to work with. Any suggestions?
You can set up a formula in column E that contains:
=IF(OR(C2=X,D2=X),1,"")
where X equals the team number. Then you can fill down through all 200 games. Then you need to Filter on Column E where it equals 1 to view all the games for team X
Related
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 an easy-to-append monthly purchase log:
month prod count
-----------------
jan water 10
jan bread 20
feb bread 2
feb water 1
And I want to get a friendlier summary table:
prod jan feb
-------------
water 10 1
bread 20 2
Any idea how I can get this raport with new months in log appearing automatically as new columns?
I managed to get the month heads with a =ArrayFormula(TRANSPOSE(UNIQUE(FILTER(log!A2:A, log!A2:A<>"")))) and I am ok with entering the prod column by hand but I only managed to have a formula per column for count. And that means I need to drag the formula with each new month added to the log...
Any ideas? Thanks!
Try this formula:
=QUERY(A:C,"select B, sum(C) where A <> '' group by B pivot A")
See more info here:
https://developers.google.com/chart/interactive/docs/querylanguage
Use number of months instead of names to get 1, 2, 3 from feb, jan ordered alphabetically
I have a client that's giving me data sets that are broken down into quarters, periods (a block of four weeks in a quarter), and weeks. I'm writing a quick reference algorithm to return the quarter, period, week given a date and year and vise versa.
Their data is always broken down into 52 weeks, where week 1 always contains Jan 1st and starts with the Monday before or at Jan 1st. This is how they handle the 365 / 7 = 52.142857 conundrum.
So, is there a gem or built in function (cweek returns 1-53), that would give me a week number based on the premise that week 1 always contains Jan 1st or do I need to design something additional?
Way 1. Date#strftime
Date.new(2016,1,1).strftime("%U").to_i + 1 # week starts with Sunday
Date.new(2016,1,1).strftime("%W").to_i + 1 # week starts with Monday
Way 2. Date#cweek
Date.new(2016,1,1).cweek % 53 + 1 # week starts with Monday
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.