polars.DataFrame.n_unique#
- DataFrame.n_unique(subset: str | Expr | Sequence[str | Expr] | None = None) int [source]#
Return the number of unique rows, or the number of unique row-subsets.
- Parameters:
- subset
One or more columns/expressions that define what to count; omit to return the count of unique rows.
Notes
This method operates at the
DataFrame
level; to operate on subsets at the expression level you can make use of struct-packing instead, for example:>>> expr_unique_subset = pl.struct("a", "b").n_unique()
If instead you want to count the number of unique values per-column, you can also use expression-level syntax to return a new frame containing that result:
>>> df = pl.DataFrame( ... [[1, 2, 3], [1, 2, 4]], schema=["a", "b", "c"], orient="row" ... ) >>> df_nunique = df.select(pl.all().n_unique())
In aggregate context there is also an equivalent method for returning the unique values per-group:
>>> df_agg_nunique = df.group_by("a").n_unique()
Examples
>>> df = pl.DataFrame( ... { ... "a": [1, 1, 2, 3, 4, 5], ... "b": [0.5, 0.5, 1.0, 2.0, 3.0, 3.0], ... "c": [True, True, True, False, True, True], ... } ... ) >>> df.n_unique() 5
Simple columns subset.
>>> df.n_unique(subset=["b", "c"]) 4
Expression subset.
>>> df.n_unique( ... subset=[ ... (pl.col("a") // 2), ... (pl.col("c") | (pl.col("b") >= 2)), ... ], ... ) 3