code review changes

doc/user_guide/03_metadata.md

@@ -193,13 +193,14 @@ print(tsgroup.coords)
 ## Using metadata to slice objects
 Metadata can be used to slice or filter objects based on metadata values.
 ```{code-cell} ipython3
-print(tsgroup[tsgroup.label == "good"])
+print(tsgroup[tsgroup.label == "A"])
 ```
 
 ## `groupby`: Using metadata to group objects
 Similar to pandas, metadata can be used to group objects based on one or more metadata columns using the object method `groupby`, where the first argument is the metadata columns name(s) to group by. This function returns a dictionary with keys corresponding to unique groups and values corresponding to object indices belonging to each group.
 ```{code-cell} ipython3
-tsgroup.groupby("region")
+print(tsgroup,"\n")
+print(tsgroup.groupby("region"))
 ```
 
 Grouping by multiple metadata columns should be passed as a list.
@@ -215,7 +216,8 @@ tsgroup.groupby("region", get_group="hpc")
 ## `groupby_apply`: Applying functions to object groups
 The `groupby_apply` object method allows a specific function to be applied to object groups. The first argument, same as `groupby`, is the metadata column(s) used to group the object. The second argument is the function to apply to each group. If only these two arguments are supplied, it is assumed that the grouped object is the first and only input to the applied function. This function returns a dictionary, where keys correspond to each unique group, and values correspond to the function output on each group.
 ```{code-cell} ipython3
-tsdframe.groupby_apply("label", np.mean)
+print(tsdframe,"\n")
+print(tsdframe.groupby_apply("label", np.mean))
 ```
 
 If the applied function requires additional inputs, these can be passed as additional keyword arguments into `groupby_apply`.

pynapple/core/metadata_class.py

@@ -329,12 +329,12 @@ def get_info(self, key):
 
     def groupby(self, by, get_group=None):
         """
-        Group pynapple object by metadata column(s).
+        Group pynapple object by metadata name(s).
 
         Parameters
         ----------
         by : str or list of str
-            Metadata column name(s) to group by.
+            Metadata name(s) to group by.
         get_group : dictionary key, optional
             Name of the group to return.
 
@@ -346,7 +346,7 @@ def groupby(self, by, get_group=None):
         Raises
         ------
         ValueError
-            If metadata column does not exist.
+            If metadata name does not exist.
         """
         if isinstance(by, str) and by not in self.metadata_columns:
             raise ValueError(
@@ -383,7 +383,7 @@ def groupby_apply(self, by, func, input_key=None, **func_kwargs):
         Parameters
         ----------
         by : str or list of str
-            Metadata column name(s) to group by.
+            Metadata name(s) to group by.
         func : function
             Function to apply to each group.
         input_key : str or None, optional

pynapple/core/time_series.py

@@ -1487,31 +1487,31 @@ def get_info(self, key):
         l2          x         x         y
         dtype: float64, shape: (5, 3)
 
-        To access a single metadata column:
+        To access a single metadata row (transposed to column):
 
         >>> tsdframe.get_info("l1")
         0    1
         1    2
         2    3
         Name: l1, dtype: int64
 
-        To access multiple metadata columns:
+        To access multiple metadata rows (transposed to columns):
 
         >>> tsdframe.get_info(["l1", "l2"])
            l1 l2
         0   1  x
         1   2  x
         2   3  y
 
-        To access metadata of a single column:
+        To access metadata of a single column (transposed to row):
 
         >>> tsdframe.get_info(0)
         rate    0.667223
         l1             1
         l2             x
         Name: 0, dtype: object
 
-        To access metadata of multiple columns:
+        To access metadata of multiple columns (transposed to rows):
 
         >>> tsdframe.get_info([0, 1])
                rate  l1 l2
@@ -1573,12 +1573,12 @@ def groupby(self, by, get_group=None):
         <BLANKLINE>
         dtype: float64, shape: (5, 3)
 
-        Grouping by a single column:
+        Grouping by a single row:
 
         >>> tsdframe.groupby("l2")
         {'x': [0, 1], 'y': [2]}
 
-        Grouping by multiple columns:
+        Grouping by multiple rows:
 
         >>> tsdframe.groupby(["l1","l2"])
         {(1, 'x'): [0], (2, 'x'): [1], (2, 'y'): [2]}
@@ -1604,16 +1604,19 @@ def groupby(self, by, get_group=None):
         Filtering to a specific group using the get_group argument:
 
         >>> tsdframe.groupby("l2", get_group="x")
-        Time (s)    0         1         2
-        ----------  --------  --------  --------
-        0.0         1.0       1.0       1.0
-        1.0         1.0       1.0       1.0
+        Time (s)    0         1
+        ----------  --------  --------
+        0.0         1.0       1.0
+        1.0         1.0       1.0
+        2.0         1.0       1.0
+        3.0         1.0       1.0
+        4.0         1.0       1.0
         Metadata
-        --------    --------  --------  --------
-        l1          1         2         2
-        l2          x         x         y
+        --------    --------  --------
+        l1          1         2
+        l2          x         x
         <BLANKLINE>
-        dtype: float64, shape: (2, 3)
+        dtype: float64, shape: (5, 2)
         """
         return _MetadataMixin.groupby(self, by, get_group)