@@ -248,9 +248,11 @@ def min(self):
248248 def value_counts (self , normalize = False , sort = True , ascending = False ,
249249 bins = None , dropna = True ):
250250 """
251- Returns object containing counts of unique values. The resulting object
252- will be in descending order so that the first element is the most
253- frequently-occurring element. Excludes NA values.
251+ Returns object containing counts of unique values.
252+
253+ The resulting object will be in descending order so that the
254+ first element is the most frequently-occurring element.
255+ Excludes NA values by default.
254256
255257 Parameters
256258 ----------
@@ -264,8 +266,8 @@ def value_counts(self, normalize=False, sort=True, ascending=False,
264266 bins : integer, optional
265267 Rather than count values, group them into half-open bins,
266268 a convenience for pd.cut, only works with numeric data
267- dropna : boolean, default False
268- Don't include counts of NaN
269+ dropna : boolean, default True
270+ Don't include counts of NaN.
269271
270272 Returns
271273 -------
@@ -289,7 +291,14 @@ def unique(self):
289291
290292 def nunique (self , dropna = True ):
291293 """
292- Return count of unique elements in the object. Excludes NA values.
294+ Return number of unique elements in the object.
295+
296+ Excludes NA values by default.
297+
298+ Parameters
299+ ----------
300+ dropna : boolean, default True
301+ Don't include NaN in the count.
293302
294303 Returns
295304 -------
0 commit comments