If you like to check for yourself or investigate further, take a look at the notebook I’ve used to create these benchmarks. Lastly, and often times most importantly, especially for memory reduction and therefore for speeding up the subsequent steps in your workflow, klib.data_cleaning() also optimizes the datatypes as we saw in the table above.klib library artifact, which can be consumed by Kotlin/Native itself as a dependency but cannot be executed or used as a native library. By default, a Kotlin/Native target is compiled down to a. If you are dealing with data where duplicates add value, consider setting drop_duplicates=False. Check out the new experimental Kotlin/Native DSL, it should be more efficient and easier to use. drops duplicate rows: This is a straightforward drop of entirely duplicate rows.Other examples are “download_date” or indicator variables which are identical for all entries. This comes in handy when columns such as “year” are included while you’re just looking at a single year. removes single valued columns: As the name states, this removes columns in which each cell contains the same value. The default is to drop columns and rows with more than 90% of the values missing.
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