DOC: update the pandas.DataFrame.all docstring

pandas/core/generic.py

@@ -7584,11 +7584,10 @@ def _add_numeric_operations(cls):
         cls.any = _make_logical_function(
             cls, 'any', name, name2, axis_descr,
             'Return whether any element is True over requested axis',
-            nanops.nanany)
+            nanops.nanany, '', '')
         cls.all = _make_logical_function(
-            cls, 'all', name, name2, axis_descr,
-            'Return whether all elements are True over requested axis',
-            nanops.nanall)
+            cls, 'all', name, name2, axis_descr, _all_doc,
+            nanops.nanall, _all_examples, _all_see_also)
 
         @Substitution(outname='mad',
                       desc="Return the mean absolute deviation of the values "
@@ -7845,25 +7844,78 @@ def _doc_parms(cls):
 %(outname)s : %(name1)s or %(name2)s (if level specified)\n"""
 
 _bool_doc = """
-
 %(desc)s
 
 Parameters
 ----------
 axis : %(axis_descr)s
 skipna : boolean, default True
     Exclude NA/null values. If an entire row/column is NA, the result
-    will be NA
+    will be NA.
 level : int or level name, default None
     If the axis is a MultiIndex (hierarchical), count along a
-    particular level, collapsing into a %(name1)s
+    particular level, collapsing into a %(name1)s.
 bool_only : boolean, default None
     Include only boolean columns. If None, will attempt to use everything,
     then use only boolean data. Not implemented for Series.
+**kwargs : any, default None
+    Additional keywords have no affect but might be accepted for
+    compatibility with numpy.
 
 Returns
 -------
-%(outname)s : %(name1)s or %(name2)s (if level specified)\n"""
+%(outname)s : %(name1)s or %(name2)s (if level specified)
+
+%(examples)s
+%(see_also)s"""
+
+_all_doc = """\
+Return whether all elements are True over series or dataframe axis.
+
+Returns True if all elements within a series or along a dataframe
+axis are non-zero, not-empty or not-False."""
+
+_all_examples = """\
+Examples
+--------
+Series
+
+>>> pd.Series([True, True]).all()
+True
+>>> pd.Series([True, False]).all()
+False
+
+Dataframes
+
+Create a dataframe from a dictionary.
+
+>>> df = pd.DataFrame({'col1': [True, True], 'col2': [True, False]})
+>>> df
+   col1   col2
+0  True   True
+1  True  False
+
+Default behaviour checks if column-wise values all return True.
+
+>>> df.all()
+col1     True
+col2    False
+dtype: bool
+
+Adding axis=1 argument will check if row-wise values all return True.
+
+>>> df.all(axis=1)
+0     True
+1    False
+dtype: bool
+"""
+
+_all_see_also = """\
+See also
+--------
+pandas.Series.all : Return True if all elements are True
+pandas.DataFrame.any : Return True if one (or more) elements are True
+"""
 
 _cnum_doc = """
 
@@ -8046,9 +8098,10 @@ def cum_func(self, axis=None, skipna=True, *args, **kwargs):
     return set_function_name(cum_func, name, cls)
 
 
-def _make_logical_function(cls, name, name1, name2, axis_descr, desc, f):
+def _make_logical_function(cls, name, name1, name2, axis_descr, desc, f,
+                           examples, see_also):
     @Substitution(outname=name, desc=desc, name1=name1, name2=name2,
-                  axis_descr=axis_descr)
+                  axis_descr=axis_descr, examples=examples, see_also=see_also)
     @Appender(_bool_doc)
     def logical_func(self, axis=None, bool_only=None, skipna=None, level=None,
                      **kwargs):