chore: Derive-Risk metadata after org transfer

docs/conf.py

@@ -19,13 +19,13 @@
 # -- Project information -----------------------------------------------------
 
 project = 'pyfair'
-copyright = '2023, Hive Systems, LLC'
-author = 'Hive Systems'
+copyright = '2023-2026, Quant LLC'
+author = 'Derive'
 
 # The short X.Y version
-version = '0.1.12'
+version = '0.1.14'
 # The full version, including alpha/beta/rc tags
-release = '0.1-alpha.12'
+release = '0.1-alpha.14'
 
 
 # -- General configuration ---------------------------------------------------
@@ -137,7 +137,7 @@
 #  author, documentclass [howto, manual, or own class]).
 latex_documents = [
     (master_doc, 'pyfair.tex', 'pyfair Documentation',
-     'Hive Systems', 'manual'),
+     'Derive', 'manual'),
 ]
 
 

pyfair/_version.py

@@ -1 +1 @@
-__version__ = "0.1-alpha.12"
+__version__ = "0.1-alpha.14"

pyfair/report/base_report.py

@@ -22,6 +22,14 @@
 from ..utility.beta_pert import FairBetaPert
 
 
+def _dataframe_elementwise(df, func):
+    """Element-wise transform; pandas 2.1+ uses map, older uses applymap."""
+    applymap = getattr(df, "applymap", None)
+    if applymap is not None:
+        return applymap(func)
+    return df.map(func)
+
+
 class FairBaseReport(object):
     """A base class for creating FairModel and FairMetaModel reports
 
@@ -262,8 +270,8 @@ def _get_overview_table(self, model_or_models):
         risk_results = risk_results.agg(["mean", "std", "min", "max"])
         risk_results.index = ["Mean", "Stdev", "Minimum", "Maximum"]
         # Format risk results into dataframe
-        overview_df = risk_results.applymap(
-            lambda x: self._format_strings["Risk"].format(x)
+        overview_df = _dataframe_elementwise(
+            risk_results, lambda x: self._format_strings["Risk"].format(x)
         )
         overview_df.loc["Simulations"] = [
             "{0:,.0f}".format(len(model.export_results()))
@@ -321,7 +329,9 @@ def _get_model_parameter_table(self, model):
             # On a column basis
             axis=1,
         )
-        param_df = param_df.applymap(lambda x: "" if "nan" in x else x)
+        param_df = _dataframe_elementwise(
+            param_df, lambda x: "" if "nan" in x else x
+        )
         # Do not truncate our base64 images.
         pd.set_option("display.max_colwidth", None)
         # Create our distribution icons as strings in table

setup.py

@@ -2,7 +2,7 @@
 
 setup(
     name="pyfair",
-    version="0.1-alpha.13",
+    version="0.1-alpha.14",
     description="Open FAIR Monte Carlo creator",
     long_description="""
         Factor Analysis of Information Risk (Open FAIR) model in Python.
@@ -18,16 +18,16 @@
         "Open FAIR" is a trademark of the Open Group.
 
     """,
-    author="Hive Systems",
-    author_email="pyfair@hivesystems.com",
+    author="Derive",
+    author_email="pyfair@deriverisk.com",
     packages=[
         "pyfair",
         "pyfair.model",
         "pyfair.report",
         "pyfair.utility",
     ],
     license="MIT",
-    url="https://github.com/Hive-Systems/pyfair",
+    url="https://github.com/Derive-Risk/pyfair",
     keywords=["FAIR", "risk", "monte carlo", "cyber risk", "risk analysis"],
     classifiers=[
         "Development Status :: 3 - Alpha",