Optional fillna in exclude_all_nan_cols.

The default behaviour has been changed to be of more general use.
The previous default was to fill the NaNs not belonging to
all NaN columns with 0, which was suitable for standardized usage
biases (which are expected to be centered on 0), but we might prefer
to keep NaNs if we want to later replace those values with something
else when dealing with other usage metrics than biases. If a single
filling value is desired, it can be set using argument fill_other_nas.

Implementation detail: The fillna still happens, but with a default
value of np.nan, which hopefully should be OK.

libcodonusage/__init__.py

 __copyright__ = "Copyright (C) 2022-2023 Blaise Li"
 __licence__ = "GNU GPLv3"
-__version__ = "0.27.5"
+__version__ = "0.28.0"
 from .libcodonusage import (
     aa2colour,
     aa_usage,

libcodonusage/libcodonusage.py

@@ -794,27 +794,28 @@ across genes) so that they are more comparable between amino-acids.
     return standardized_aa_usage_biases
 
 
-def exclude_all_nan_cols(standardized_usage_biases):
+def exclude_all_nan_cols(usage_table, fill_other_nas=np.nan):
     """
-    Detect columns in *standardized_usage_biases* that contain only NaNs
-    and remove them  from the table.
+    Detect columns in *usage_table* that contain only NaNs
+    and remove them from the table. Other NaN values are replaced
+    with *fill_other_nas*.
     """
     render_md("""
-Standardization may result in division by zero for usage biases
+Standardization may result in division by zero for usage data
 that have a zero standard deviation.
 This is expected to be the case for "by amino-acid" usage biases
 for codons corresponding to amino-acids having only one codon:
 methionine (M) and tryptophan (W).
 """)
-    all_nan_cols = standardized_usage_biases.columns[
-        standardized_usage_biases.isna().all()]
+    all_nan_cols = usage_table.columns[
+        usage_table.isna().all()]
     if len(all_nan_cols):
         render_md("The following columns contain only NaNs:")
         display(all_nan_cols)
         render_md("This likely resulted from a division by zero.")
         render_md("These columns will be excluded.")
     return (
-        standardized_usage_biases.drop(columns=all_nan_cols).fillna(0),
+        usage_table.drop(columns=all_nan_cols).fillna(fill_other_nas),
         all_nan_cols)