Fix docs

Author: alejoe91

doc/how_to/combine_recordings.rst

@@ -1,4 +1,4 @@
-Combine Recordings in SpikeInterface
+Combine recordings in SpikeInterface
 ====================================
 
 In this tutorial we will walk through combining multiple recording objects. Sometimes this occurs due to hardware

doc/how_to/load_matlab_data.rst

@@ -1,4 +1,4 @@
-Export MATLAB Data to Binary & Load in SpikeInterface
+Export MATLAB data to binary & load in SpikeInterface
 ========================================================
 
 In this tutorial, we will walk through the process of exporting data from MATLAB in a binary format and subsequently loading it using SpikeInterface in Python.

doc/how_to/load_your_data_into_sorting.rst

@@ -1,5 +1,5 @@
-Load Your Own Data into a Sorting
-=================================
+Load your own data into a Sorting object
+========================================
 
 Why make a :code:`Sorting`?
 

doc/how_to/process_by_channel_group.rst

@@ -1,4 +1,4 @@
-Process a Recording by Channel Group
+Process a recording by channel group
 ====================================
 
 In this tutorial, we will walk through how to preprocess and sort a recording

doc/how_to/viewers.rst

@@ -1,4 +1,4 @@
-Visualize Data
+Visualize data
 ==============
 
 There are several ways to plot signals (raw, preprocessed) and spikes.

src/spikeinterface/core/sortinganalyzer.py

@@ -2245,9 +2245,15 @@ def _save_data(self):
                 elif HAS_PANDAS and isinstance(ext_data, pd.DataFrame):
                     df_group = extension_group.create_group(ext_data_name)
                     # first we save the index
-                    df_group.create_dataset(name="index", data=ext_data.index.to_numpy())
+                    indices = ext_data.index.to_numpy()
+                    if indices.dtype.kind == "O":
+                        indices = indices.astype(str)
+                    df_group.create_dataset(name="index", data=indices)
                     for col in ext_data.columns:
-                        df_group.create_dataset(name=col, data=ext_data[col].to_numpy())
+                        col_data = ext_data[col].to_numpy()
+                        if col_data.dtype.kind == "O":
+                            col_data = col_data.astype(str)
+                        df_group.create_dataset(name=col, data=col_data)
                     df_group.attrs["dataframe"] = True
                 else:
                     # any object