Comments (3)
See also:
We're generally in favour of adding n_unique
, but there are performance considerations for large data frames; I think the only thing holding it back from being added is that nobody has actually quantified the impact on large frames yet 😉
If the impact is relatively minimal then we could certainly add this. Alternatively we could perhaps provide approx_n_unique
results instead (labeled appropriately) if the impact is more significant.
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I am in favor of a unique count! However, I would call it "distinct count" since unique and distinct are not the same 😀
For the others I am not sure. I can see them being usefull for specific use cases. Maybe add some parameters to describe
to optionally add more statistics.
I really like the idea of describe
being an one-stop-shop for all commonly used statistics instead of sprinkling around 100x cells in the notebook all the time 🤣
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Yes, I have found describe to not be enough for real life application (you know those hardcoded "999999"). I'd like to get other opinions, then i'll try to handle it.
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Related Issues (20)
- exception thrown if converting arrow Table with struct and dictionary columns to polar dataframe
- converting pandas to Polars drops column if its name, when converted to string, matches another column's name
- pl.format should be clear it will return null when one of the arguments is null
- Off-by-one error when casting to Decimal with set precision
- Importing pyarrow after polars causes `SIGSEGV` HOT 4
- Polars assumes microseconds instead of reading numpy timedelta units HOT 1
- Cannot create Array column containing large u64 value
- Multipling a Decimal by Int returns Int type HOT 2
- Split out `Expr.top_k` from `Expr.top_k_by`
- `pl.Datetime` `time_zone` parameter has no type or value check HOT 6
- Cast from `pl.Date` to `pl.Datetime` silently returns incorrect value when new dtype cannot hold value HOT 2
- exception thrown if converting chunked arrow Table with struct and dictionary columns to polar Dataframe
- Panic when constructing Series with dtype `Duration('ms')` with large `timedelta` objects
- Can the separator of the read csv function support regular splitting? HOT 5
- Casting float to Decimal fails silently HOT 2
- Use parquet statistics when collecting column statistics from scanned parquet HOT 2
- Excessive Memory Consumption During Rolling Operations on Large DataFrames
- write_database() - Insert many rows with sql server using fast_executemany HOT 3
- fill_null doesn't support expr HOT 6
- `dt.total_nanoseconds` and `dt.total_microseconds` may overflow silently
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