@@ -247,9 +247,11 @@ def min(self):
247247 def value_counts (self , normalize = False , sort = True , ascending = False ,
248248 bins = None , dropna = True ):
249249 """
250- Returns object containing counts of unique values. The resulting object
251- will be in descending order so that the first element is the most
252- frequently-occurring element. Excludes NA values.
250+ Returns object containing counts of unique values.
251+
252+ The resulting object will be in descending order so that the
253+ first element is the most frequently-occurring element.
254+ Excludes NA values by default.
253255
254256 Parameters
255257 ----------
@@ -263,8 +265,8 @@ def value_counts(self, normalize=False, sort=True, ascending=False,
263265 bins : integer, optional
264266 Rather than count values, group them into half-open bins,
265267 a convenience for pd.cut, only works with numeric data
266- dropna : boolean, default False
267- Don't include counts of NaN
268+ dropna : boolean, default True
269+ Don't include counts of NaN.
268270
269271 Returns
270272 -------
@@ -288,7 +290,14 @@ def unique(self):
288290
289291 def nunique (self , dropna = True ):
290292 """
291- Return count of unique elements in the object. Excludes NA values.
293+ Return number of unique elements in the object.
294+
295+ Excludes NA values by default.
296+
297+ Parameters
298+ ----------
299+ dropna : boolean, default True
300+ Don't include NaN in the count.
292301
293302 Returns
294303 -------
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