Need to divide a Dataframe in various tables using multiple categories and date time - pandas
this is my first time asking a question here, so if I'm doing something wrong please guide me to the right place. I have a big and clean dataset. (29000+ , 24). The thing is that I have to calculate the churn rate based on 4 different categorical columns, and I'm given just 1 column that contains the subs for a given period. I have a date column too. My idea on calculating the churn is to do
churn_rate= (Sub_start_period-Sub_end_period)/Sub_start_period*100
The Problem
I don't know how to group the data using these 4 different categorical variables. Also If I manage to do so I would end up with more than 200 different tables, so I don't believe this would be a good approach.
My goal is able to predict the churn rate using the information in the table but I should be able to determine the churn rate based on these variables. The churn is not given, it has to be calculated, so I'm having problems here as I can't think of a way of working through this.
Related
Power BI: Measure for Date difference depending on other columns
I hope everybody is doing fine! :) I have a table like the one in my "Example" picture. Let's say it is data about certain products and a certain assembly status (i.e. column "Status"). In "Status Date" I can see the date on which the product has been in the specific status. I only added dates for ID 1 to make the table easier. Table What I am looking for is a measure in Power BI to calculate the difference (in days or month doesn't matter) between the dates. I don't want to use the number in the Status (e.g. 1 for Stat 1) to identify the order of the dates. To make it even harder, I may want to filter out Stat 2 for some reason. In that case I want the measure to automatically adapt and calculate the difference between Stat 3 and Stat 1. I have the feeling that this is possible in a single formular using a measure which would be the optimal solution from my point of view. I hope there's someone who can help me! Thanks in advance. Daniel
Is it possible to use SQL to show the average of some values in one column and then in subsequent columns display the individual values?
I have a bunch of data and I want the output to display an average of all the data points but also the individual data points in subsequent columns. Ideally it would look something like this: Compound | Subject | Avg datapoint | Datapoint Experiment 1 | Datapoint Exp 2 | ... ..........XYZ......|.....ABC....|............40...............|...............20..............................|...............60...............|...... ..........TUV......|.....ABC....|............30...............|...............20..............................|...............40...............|...... ..........TUV......|.....DEF....|............20...............|...............10..............................|...............30...............|...... One problem I'm running in to is that I get repetitive lines of information. Another is that I have some rows pulling in info that doesn't apply, such that some of the individual datapoints in, say, row 2 would have info from subject DEF when I only want it to have info from subject ABC. I hope this makes sense! I'm currently using inner join with a ton of where qualifiers. I'm close but not quite there. Any help is appreciate and let me know if I can provide additional info to help you help me.
The SQL language has a very strict rule requiring you to know the exact number of columns for your result set in advance, before looking at any data in your tables. Therefore, if this average is based off a known fixed number of columns, or if the number of potential columns is reasonably small, where you can manually setup placeholders, then this will be possible. The key search terms to learn how to do this is "conditional aggregation", where you may also need to join the table to itself for each field. Otherwise, you will need to pivot and aggregate your data in your client code or reporting tool.
How to populate all possible combination of values in columns, using Spark/normal SQL
I have a scenario, where my original dataset looks like below Data: Country,Commodity,Year,Type,Amount US,Vegetable,2010,Harvested,2.44 US,Vegetable,2010,Yield,15.8 US,Vegetable,2010,Production,6.48 US,Vegetable,2011,Harvested,6 US,Vegetable,2011,Yield,18 US,Vegetable,2011,Production,3 Argentina,Vegetable,2010,Harvested,15.2 Argentina,Vegetable,2010,Yield,40.5 Argentina,Vegetable,2010,Production,2.66 Argentina,Vegetable,2011,Harvested,15.2 Argentina,Vegetable,2011,Yield,40.5 Argentina,Vegetable,2011,Production,2.66 Bhutan,Vegetable,2010,Harvested,7 Bhutan,Vegetable,2010,Yield,35 Bhutan,Vegetable,2010,Production,5 Bhutan,Vegetable,2011,Harvested,2 Bhutan,Vegetable,2011,Yield,6 Bhutan,Vegetable,2011,Production,3 Image of the above csv: Now there is a very small country lookup table which has all possible countries the source data can come with, listed. PFB: I want to have the output data's number of columns always fixed (this is to ensure the reporting/visualization tool doesn't get dynamic number columns with every day's new source data ingestions depending on the varying distinct number of countries present). So, I've to somehow join the source data with the country_lookup csv and populate all those columns with default value as F. Every country column would be binary with T or F being the possible values. The original dataset from the above has to be converted into below: Data (I've kept the Amount field unsolved for column Type having Derived Yield as is, rather than calculating them below for a better understanding and for you to match with the formulae): Country,Commodity,Year,Type,Amount,US,Argentina,Bhutan,India,Nepal,Bangladesh US,Vegetable,2010,Harvested,2.44,T,F,F,F,F,F US,Vegetable,2010,Yield,15.8,T,F,F,F,F,F US,Vegetable,2010,Production,6.48,T,F,F,F,F,F US,Vegetable,2010,Derived Yield,(2.44+15.2)/(6.48+2.66),T,T,F,F,F,F US,Vegetable,2010,Derived Yield,(2.44+7)/(6.48+5),T,F,T,F,F,F US,Vegetable,2010,Derived Yield,(2.44+15.2+7)/(6.48+2.66+5),T,T,T,F,F,F US,Vegetable,2011,Harvested,6,T,F,F,F,F,F US,Vegetable,2011,Yield,18,T,F,F,F,F,F US,Vegetable,2011,Production,3,T,F,F,F,F,F US,Vegetable,2011,Derived Yield,(6+10)/(3+9),T,T,F,F,F,F US,Vegetable,2011,Derived Yield,(6+2)/(3+3),T,F,T,F,F,F US,Vegetable,2011,Derived Yield,(6+10+2)/(3+9+3),T,T,T,F,F,F Argentina,Vegetable,2010,Harvested,15.2,F,T,F,F,F,F Argentina,Vegetable,2010,Yield,40.5,F,T,F,F,F,F Argentina,Vegetable,2010,Production,2.66,F,T,F,F,F,F Argentina,Vegetable,2010,Derived Yield,(2.44+15.2)/(6.48+2.66),T,T,F,F,F,F Argentina,Vegetable,2010,Derived Yield,(15.2+7)/(2.66+5),F,T,T,F,F,F Argentina,Vegetable,2010,Derived Yield,(2.44+15.2+7)/(6.48+2.66+5),T,T,T,F,F,F Argentina,Vegetable,2011,Harvested,10,F,T,F,F,F,F Argentina,Vegetable,2011,Yield,90,F,T,F,F,F,F Argentina,Vegetable,2011,Production,9,F,T,F,F,F,F Argentina,Vegetable,2011,Derived Yield,(6+10)/(3+9),T,T,F,F,F,F Argentina,Vegetable,2011,Derived Yield,(10+2)/(9+3),F,T,T,F,F,F Argentina,Vegetable,2011,Derived Yield,(6+10+2)/(3+9+3),T,T,T,F,F,F Bhutan,Vegetable,2010,Harvested,7,F,F,T,F,F,F Bhutan,Vegetable,2010,Yield,35,F,F,T,F,F,F Bhutan,Vegetable,2010,Production,5,F,F,T,F,F,F Bhutan,Vegetable,2010,Derived Yield,(2.44+7)/(6.48+5),T,F,T,F,F,F Bhutan,Vegetable,2010,Derived Yield,(15.2+7)/(2.66+5),F,T,T,F,F,F Bhutan,Vegetable,2010,Derived Yield,(2.44+15.2+7)/(6.48+2.66+5),T,T,T,F,F,F Bhutan,Vegetable,2011,Harvested,2,F,F,T,F,F,F Bhutan,Vegetable,2011,Yield,6,F,F,T,F,F,F Bhutan,Vegetable,2011,Production,3,F,F,T,F,F,F Bhutan,Vegetable,2011,Derived Yield,(2.44+7)/(6.48+5),T,F,T,F,F,F Bhutan,Vegetable,2011,Derived Yield,(10+2)/(9+3),F,T,T,F,F,F Bhutan,Vegetable,2011,Derived Yield,(6+10+2)/(3+9+3),T,T,T,F,F,F The image of the above expected output data for a structured look at it: Part 1 - Part 2 - Formulae for populating Amount Field for Derived Type: Derived Amount = Sum of Harvested of all countries with T (True) grouped by Year and Commodity columns divided by Sum of Production of all countries with T (True)grouped by Year and Commodity columns. So, the target is to have a combination of all the countries from source and calculate the sum of respective Harvested and Production values which then has to be divided. The commodity can be more than one in the actual scenario for any given country, but that should not bother as the summation of amount happens on grouped commodity and year. Note: The users in the frontend can select any combination of countries. The sole purpose of doing it in the backend rather than dynamically doing it in the frontend is because AWS QuickSight (our visualisation tool), even though can populate sum on selected column filters but doesn't yet support calculation on those derived summed fields. Hence, the entire calculation of all combination of countries has to be pre-populated (very naive approach) in order to make it available in report on dynamic users selection of countries. Also if you've any better approach (than the above naive approach mentioned in note) to solve this problem, you are most welcome to guide me. I've also posted a question on the same problem without writing my expected approach for experts to show me the path on how we can solve this kind of a problem better than this naive approach. If you want to help solve it with some other technique, you're most welcome, here is the link to that question. Any help shall be greatly acknowledged.
DAX sum different DateTime
I have a problem here, i would like to sum the work time from my employee based on the data (time2 - time 1) daily and here is my query: Effective Minute Work Time = 24. * 60 * (LASTNONBLANK(time2,0) -FIRSTNONBLANK(time1,0)) It works daily, but if i drill up to weekly / monthly data it show the wrong sum as it shown below : What i want is summary of minute between daily different times (time2-time1) Thanks for your help :)
You have several approaches you can take: the hard way or the easier way :). The harder (at least for me :)) is to use DAX to do this. You would: 1) create a date table, 2) Use the DAX calculate function to evaluate your last non-blank and first non-blank values (you might need to use calculate table, but I'm not sure; DAX experts jump in). Then subtract one vs. the other. This will give you correct values for a given day for a given person. You can enforce the latter condition by putting a 'has one value' guard on the person name so that your measure informs the report author if they're not using it right. Doing the same for dates is a little trickier. In the example you show you are including the date in the row grouping. But if you change your mind and want instead to have 'total hours worked by person' or 'total hours worked by everyone' you're not done with modelling yet. Your next step is to use calculate table in combination with calculate to create a measure that returns the total. You'll use calculate table so you evaluate each date and the hours worked on that date by person. Then you'll use calculate to summarize that all down to a single number. If you're not careful with your DAX (or report authoring) you might mix which person you're summarizing for so that your first/last non blank are not at the person level. It gets intense quickly. Your easier solution, though it might be more limited in its application - depends really on your scenario - is to use the query to transform the data into a summary by day and person using the group by command. This will give you a row per person per day with their start and end times. Then you can quickly calculate the hours worked on that day. Then you can quite easily build visuals on top of the summary data. Of course you give up some of the flexibility of the having a proper data model. However if you have a date table, a person table, and your summary table and then setup your relationships correctly you can achieve answers to the most common questions.
track sales for week/month and find the best sellers
Lets say I have a website that sells widgets. I would like to do something similar to a tag cloud tracking best sellers. However, due to constantly aquiring and selling new widgets, I would like the sales to decay on a weekly time scale. I'm having problems puzzling out how store and manipulate this data and have it decay properly over time so that something that was an ultra hot item 2 months ago but has since tapered off doesn't show on top of the list over the current best sellers. What would be the logic and database design for this?
Part 1: You have to have tables storing the data that you want to report on. Date/time sold is obviously key. If you need to work in decay factors, that raises the question: for how long is the data good and/or relevant? At what point in time as the "value" of the data decayed so much that you no longer care about it? When this point is reached for any given entry in the database, what do you do--keep it there but ensure it gets factored out of all subsequent computations? Or do you archive it--copy it to a "history" table and delete it from your main "sales" table? This is relevant, as it has to be factored into your decay formula (as well as your capacity planning, annual reporting requirements, and who knows what all else.) Part 2: How much thought has been given to the decay formula that you want to use? There's no end of detail you can work into this. Options and factors to wade through include but are not limited to: Simple age-based. Everything before the cutoff date counts as 1; everything after counts as 0. Sum and you're done. What's the cutoff date? Precisly 14 days ago, to the minute? Midnight as of two Saturdays ago from (now)? Does the cutoff date depend on the item that was sold? If some items are hot but some are not, does that affect things? What if you want to emphasize some things (the expensive/hard to sell ones) over others (the fluff you'd sell anyway)? Simple age-based decays are trivial, but can be insufficient. Time to go nuclear. Perhaps you want some kind of half-life, Dr. Freeman? Everything sold is "worth" X, where the value of X is either always the same or varies on the item sold. And the value of X can decay over time. Perhaps the value of X decreased by one-half every week. Or ever day. Or every month. Or (again) it may vary depending on the item. If you do half-lifes, the value of X may never reach zero, and you're stuck tracking it forever (which is why I wrote "part 1" first). At some point, you probably need some kind of cut-off, some point after which you just don't care. X has decreased to one-tenth the intial value? Three months have passed? Either/or but the "range" depends on the inherent valud of the item? My real point here is that how you calculate your decay rate is far more important than how you store it in the database. So long as the data's there that the formalu needs to do it's calculations, you should be good. And if you only need the last month's data to do this, you should perhaps move everything older to some kind of archive table.
you could just count the sales for the last month/week/whatever, and sort your items according to that. if you want you can always add the total amonut of sold items into your formula.
You might have a table which contains the definitions of the pointing criterion (most sales, most this, most that, etc.), then for a given period, store in another table the attribution of points for each of the criterion defined in the criterion table. Obviously, a historical table will be used to store the score for each sellers for a given period or promotion, call it whatever you want. Does it help a little?