@@ -2206,18 +2206,47 @@ def isna(self):
22062206
22072207 def notna (self ):
22082208 """
2209- Inverse of isna
2209+ Detect existing (non-missing) values.
2210+
2211+ Return a boolean same-sized object indicating if the values are not NA.
2212+ Non-missing values get mapped to ``True``. Characters such as empty
2213+ strings ``''`` or :attr:`numpy.inf` are not considered NA values
2214+ (unless you set ``pandas.options.mode.use_inf_as_na = True``).
2215+ NA values, such as None or :attr:`numpy.NaN`, get mapped to ``False``
2216+ values.
22102217
22112218 .. versionadded:: 0.20.0
22122219
22132220 Returns
22142221 -------
2215- a boolean array of whether my values are not NA
2222+ numpy.ndarray
2223+ Boolean array to indicate which entries are not NA.
22162224
22172225 See also
22182226 --------
2219- notnull : alias of notna
2227+ Index.notnull : alias of notna
2228+ Index.isna: inverse of notna
22202229 pandas.notna : top-level notna
2230+
2231+ Examples
2232+ --------
2233+ Show which entries in an Index are not NA. The result is an
2234+ array.
2235+
2236+ >>> idx = pd.Index([5.2, 6.0, np.NaN])
2237+ >>> idx
2238+ Float64Index([5.2, 6.0, nan], dtype='float64')
2239+ >>> idx.notna()
2240+ array([ True, True, False])
2241+
2242+ Empty strings are not considered NA values. None is considered a NA
2243+ value.
2244+
2245+ >>> idx = pd.Index(['black', '', 'red', None])
2246+ >>> idx
2247+ Index(['black', '', 'red', None], dtype='object')
2248+ >>> idx.notna()
2249+ array([ True, True, True, False])
22212250 """
22222251 return ~ self .isna ()
22232252 notnull = notna
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