palantir | Palantir is a project to detect and analyze changes | Machine Learning library

I'm new to Angular and try to install ngx-admin template but I got these errors. how to fix these errors?

If I'm using Python Transforms in Palantir Foundry and I'm trying to run an algorithm which uses in-memory/non-spark libraries, and I want it automatically scale and work in Spark (not pandas). If I'm having a hard time writing the code and want to test and develop it locally, yet use the same code in pyspark later, how do I do this?

For a concrete example, I want to calculate the area of a geojson column which contains a polygon. Since I would need to use some libraries which arn't native to Spark (shapely and pyproj). I know that the best way (performance wise) is to use a pandas_udf (otherwise known as streaming udfs or vectorized udfs). But after reading a couple of guides, specifically Introducing Pandas UDF for PySpark, pandas user-defined functions and Modeling at Scale with Pandas UDFs w/code examples, it's still challenging to debug and get working, and it seems like I can't use break statements and there isn't a first class way to log/print.

The actual dataframe would have millions of rows (relating to millions of polygons), but for simplicity I wanted to test locally with a simple dataframe, and it scale to larger dataset later:

I'm working on my first, very basic Docker Image with Spring Boot & Gradle.

When i run it i get following error :
No auto configuration classes found in META-INF/spring.factories. If you are using a custom packaging, make sure that file is correct.

I'm using Blueprint.js v3.x and have been making use of the SASS variables. I want to leverage the $ns variable, but my app is in dark mode. The $ns variable is set to bp3 !default; on line 10 of the file above, which from what I understand means I can set it to a different value in my app.scss. It would be fine, then, to just set $ns: bp3-dark; and use this variable that way. However, in the future I want to support a dark/light mode switch; how can I, following best practice, dynamically set $ns to be the correct value based on said switch?

There are times when an incremental pipeline in Palantir Foundry has to be built as a snapshot. If the data size is large, the resources to run the build are increased to reduce run time and then the configuration is removed after first snapshot run. Is there a way to set conditional configuration? Like if pipeline is running on Incremental Mode, use default configuration of resource allocation and if not the specified set of resources.

Example: If pipeline runs as snapshot transaction, below configuration has to be applied

here is my txt file that has contained all of the lines. What I want to do is create a dictionary, and access a key, and get a list of values

I'm starting with a large zip file of csvs, which I unzipped in Palantir Foundry.

I now have a dataset which consists of multiple csvs (one for each year), where the csvs are almost the same schema but have some differences. How do I apply a schema to each of the csvs individually or normalize the schema between them?

In a Palantir Foundry Code Workbook Spark SQL node (or in the Spark console in SQL mode), this works:

Palantir Foundry's Workshop allows us to save default values for variables using the "save and publish with current variable values as default" button. Can these default values be cleared out or reset?