devin-petersohn commented on code in PR #54044:
URL: https://github.com/apache/spark/pull/54044#discussion_r2824234109
##########
python/pyspark/pandas/frame.py:
##########
@@ -4906,49 +4906,49 @@ def nunique(
rsd: float = 0.05,
) -> "Series":
"""
- Return number of unique elements in the object.
-
- Excludes NA values by default.
-
- Parameters
- ----------
- axis : int, default 0 or 'index'
- Can only be set to 0 now.
- dropna : bool, default True
- Don’t include NaN in the count.
- approx: bool, default False
- If False, will use the exact algorithm and return the exact number
of unique.
- If True, it uses the HyperLogLog approximate algorithm, which is
significantly faster
- for large amounts of data.
- Note: This parameter is specific to pandas-on-Spark and is not
found in pandas.
- rsd: float, default 0.05
- Maximum estimation error allowed in the HyperLogLog algorithm.
- Note: Just like ``approx`` this parameter is specific to
pandas-on-Spark.
-
- Returns
- -------
- The number of unique values per column as a pandas-on-Spark Series.
-
- Examples
- --------
- >>> df = ps.DataFrame({'A': [1, 2, 3], 'B': [np.nan, 3, np.nan]})
- >>> df.nunique()
- A 3
- B 1
- dtype: int64
-
- >>> df.nunique(dropna=False)
- A 3
- B 2
- dtype: int64
-
- On big data, we recommend using the approximate algorithm to speed up
this function.
- The result will be very close to the exact unique count.
-
- >>> df.nunique(approx=True)
- A 3
- B 1
- dtype: int64
Review Comment:
Yes, done!
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