Change group_spec to keyword argument, add None, [] behavior.

pandas_sequence/sequence.py

@@ -105,7 +105,7 @@ def contiguous_group_indices(df_or_series, sequence_index_level=None, sequence_c
 
     return grp_ids
 
-def sequence_df(df, lags, group_specs):
+def sequence_df(df, lags, group_specs=None):
     """
     Sequence feature data into multi-component rows.
 
@@ -132,7 +132,7 @@ def sequence_df(df, lags, group_specs):
     Named Arguments
       df: A Pandas dataframe containing a set of features for each day
       lags: A list of lags to include
-      group_specs: A list of tuples defining how groups are discovered.
+      group_specs: A list of tuples defining how groups are discovered. (Can also be None or empty list)
         'group' type specs - Group type specs specify columns, or index levels where the groups are already defined.
         'sequence' type specs - Sequence type specs specify 'sequencable' columns. These columns have a 'by-one' well ordering defined.
             This well ordering can either be implicit if you use integers, or you can pass a function which defines it.
@@ -148,10 +148,15 @@ def sequence_df(df, lags, group_specs):
         ('group', 'column', groups) - Use the groups series to define the groups to use. This is a column passed in.
         ('group', 'column', 'Group') - Use the 'Group' column of the dataframe to define the groups to use. This is a column passed in.
 
+        If group_specs is None or an empty list, no grouping is done, and the lags are computed using shift directly on the input DataFrame.
+
     returns
       A pandas dataframe containing rows of prediction and/or label data.
     """
 
+    if group_specs is None:
+        group_specs = []
+
     by = []
     level = []
     remove_columns = []