churn | Providing additional churn metrics

I have two arrays, arr1 is an array of objects and arr2 is just a regular array. I am trying to match arr2 values with the "original" value from the objects of arr1 and return the "new" value into a new resulting array. There's usually more than 2 items in arr2 and the order isn't the always same that's why I couldn't just match by the index each time.

OK, I realize the solution to this is probably something very simple, but I've wasted a lot of time trying to figure it out. As you'll likely be able to tell, I'm new to AppleScript.

I have an AppleScript being run by Automator. It clicks a button in Chrome. I have an action I need to repeat nearly 1000 times, so I'm trying to automate it. Here's what I have:

I have the following code that produces a stacked bar chart.

I would like to preserve the order of the bars (from top to bottom: Expansion, New, Contraction, Churned), while also having the legend order be in the same way.

When I change the levels of the type factor, it reorders the legend correctly, but then changes the order in the plot. How can I get the two to match?

There is a similar question here, but the accepted answer's plot and legend don't match up in the same order.

Here is some simple code to demonstrate:

I am looking to keep track of customers that are going to churn in 2019 in the order data of 2018 so that I can do some analyses such as where the customers come from, if their order size has been decreasing compared to customers that will not churn.

The orderdata in 2018 is a pandas df called 'order_data' and I have a list of customers that will churn in 2019 called 'churn_customers_2019'. In order_data there is a column called Customer_id. The list is also filled with Customer_id names of the clients that will churn.

However my logic is not running well.

I have a large customer dataset, it has things like Customer ID, Service ID, Product, etc. So the two ways we can measure churn are at a Customer-ID level, if the entire customer leaves and at a Service-ID level where maybe they cancel 2 out of 5 services.

The data looks like this, and as we can see

• Alligators stops being a customer at the end of Jan as they dont have any rows in Feb (CustomerChurn)
• Aunties stops being a customer at the end of Jan as they dont have any rows in Feb (CustomerChurn)
• Bricks continues with Apples and Oranges in Jan and Feb (ServiceContinue)
• Bricks continues being a customer but cancels two services at the end of Jan (ServiceChurn)

I am trying to write some code that creates the 'Churn' column.. I have tried

• To manually just grab lists of CustomerIDs and ServiceIDs using Set from Oct 2019, and then comparing that to Nov 2019, to find the ones that churned. This is not too slow but doesn't seem very Pythonic.

Thank you!

I'm new to SQL and AWS Timestream and I want to write a query which will give me the total time that a device is active. I then want to translate that into energy usage based on the kWh rating of the device.

The time intervals for data points are not on a fixed interval. Data looks something like this:

In the above the total active time is 8 minutes.

The kind of queries I would like to get out are something like,

• Total active time (energy usage) over the last month (or other period)
• Total active time (energy usage) per day over the last month

What query would give me this info and be tolerant of the variable intervals?

There are two paths that I'm looking at but haven't quite figured out yet,

1. Interpolate the data and fill with the value to get a new timestream with a consistent interval (then it is as simple as counting the values), or
2. Use some date/time function to look at the timestamps between the data points and add up the total time that it is active.

I've been trying to get a query right to interpolate the data, but have not succeeded yet. I'm following the pattern in the AWS Timestream SQL docs, but not quite understanding it yet.

I don't even know where to begin or where to look for examples of summing the timestamp difference. The logical process would be something like,

So am working on telecommunication dataset to create a machine learning model to predict the churn rate. When I started to create barplots I get a type error which says "Neither the x nor y variable appears to be numeric". Both x and y variables are dtype= object

My question is when creating such plots is it compulsory any one variable should be numeric? I tried to google the reason but was unable to understand, If any can help me with a proper explanation it would be great.

I cant get FB_MBReadinputs to work in Twincat, when Factory IO is sending/receiving Input and Holding registers.

First off here's my currently working snip of handling Modbus from Factory IO:

According to Firestore's Best Practices docs (below), one should avoid adding and removing snapshot listeners in quick succession. The docs state, "snapshot listeners should have a lifetime of 30 seconds or longer." However, because a snapshot subscribe and subsequent unsubscribe is controlled by the user's actions (e.g. navigation to and away from a particular page), it may not be possible to always to keep a connection open for more than 30 seconds.

As an example, my app has an Account Details page. The page has one listener the subscribes to the main details (i.e. Account Name, Primary Address, Primary Contact, etc.). The page also has several tables (e.g. Locations, Inventory, Purchase Orders, etc.) each of which has their own listener.

That being said, would it be problematic if my users navigate between several Account Details pages very quickly (since each page will be opening and closing its own set of 3-5 listeners)? If it is problematic, what type of issues will this create for my app? For instance, would Firestore simply slow down temporarily? Or could there be bigger issues with data consistency (i.e. where the snapshot temporarily shows old snapshot data while waiting for the new snapshot to prime)?

Here's what is stated in Firestore's Best Practices documentation:

Avoid frequently churning listeners, especially when your database is under significant write load.

Ideally, your application should set up all the required snapshot listeners soon after opening a connection to Cloud Firestore. After setting up your initial snapshot listeners, you should avoid quickly adding or removing snapshot listeners in the same connection.

To ensure data consistency, Cloud Firestore needs to prime each new snapshot listener from its source data and then catch up to new changes. Depending on your database's write rate, this can be an expensive operation.

Your snapshot listeners can experience increased latency if you frequently add or remove snapshot listeners to references. In general, a constantly-attached listener performs better than attaching and detaching a listener at that location for the same amount of data. For best performance, snapshot listeners should have a lifetime of 30 seconds or longer. If you encounter listener performance issues in your app, try tracking your app's listens and unlistens to determine if they may be happening too frequently.

https://firebase.google.com/docs/firestore/best-practices#realtime_updates