pyarrow.compute.rank_quantile#

pyarrow.compute.rank_quantile(input, /, sort_keys='ascending', *, null_placement=None, options=None, memory_pool=None)#

Compute quantile ranks of an array.

This function computes a quantile rank of the input array. By default, null values are considered greater than any other value and are therefore sorted at the end of the input. For floating-point types, NaNs are considered greater than any other non-null value, but smaller than null values. The results are real values strictly between 0 and 1. They are computed as in https://en.wikipedia.org/wiki/Quantile_rank but without multiplying by 100.

The handling of nulls and NaNs can be changed in RankQuantileOptions.

Parameters:

inputArray-like or scalar-like

Argument to compute function.

sort_keyssequence of (name, order, null_placement) tuples or str, default ascending

Names of field/column keys to sort the input on, along with the order each field/column is sorted in. Accepted values for order are ascending, descending. Accepted values for null_placement are at_start, at_end. The field name can be a string column name or expression. Alternatively, one can simply pass ascending or descending as a string if the input is array-like.

null_placementstr | None, default None

Where nulls in input should be sorted. Accepted values are at_start, at_end.

optionspyarrow.compute.RankQuantileOptions, optional

Alternative way of passing options.

memory_poolpyarrow.MemoryPool, optional

If not passed, will allocate memory from the default memory pool.