diff --git a/intake_esm/core.py b/intake_esm/core.py
index 4c940237..d9cb3786 100644
--- a/intake_esm/core.py
+++ b/intake_esm/core.py
@@ -127,10 +127,7 @@ def _set_groups_and_keys(self):
             internal_keys = self._grouped.groups.keys()
             public_keys = []
             for key in internal_keys:
-                if isinstance(key, str):
-                    p_key = key
-                else:
-                    p_key = self.sep.join(str(v) for v in key)
+                p_key = key if isinstance(key, str) else self.sep.join(str(v) for v in key)
                 public_keys.append(p_key)
 
         else:
@@ -206,14 +203,13 @@ def _get_aggregation_info(self):
             for column in columns:
                 self.df[column] = self.df[column].map(tuple)
 
-        aggregation_info = AggregationInfo(
+        return AggregationInfo(
             groupby_attrs,
             variable_column_name,
             aggregations,
             agg_columns,
             aggregation_dict,
         )
-        return aggregation_info
 
     def keys(self) -> List:
         """
@@ -237,10 +233,9 @@ def key_template(self) -> str:
           string template used to create catalog entry keys
         """
         if self.aggregation_info.groupby_attrs:
-            template = self.sep.join(self.aggregation_info.groupby_attrs)
+            return self.sep.join(self.aggregation_info.groupby_attrs)
         else:
-            template = self.sep.join(self.df.columns)
-        return template
+            return self.sep.join(self.df.columns)
 
     @property
     def df(self) -> pd.DataFrame:
@@ -525,8 +520,7 @@ def _repr_html_(self):
         """
         uniques = pd.DataFrame(self.nunique(), columns=['unique'])
         text = uniques._repr_html_()
-        output = f'<p><strong>{self.esmcol_data["id"]} catalog with {len(self)} dataset(s) from {len(self.df)} asset(s)</strong>:</p> {text}'
-        return output
+        return f'<p><strong>{self.esmcol_data["id"]} catalog with {len(self)} dataset(s) from {len(self.df)} asset(s)</strong>:</p> {text}'
 
     def _ipython_display_(self):
         """
@@ -760,9 +754,7 @@ def nunique(self) -> pd.Series:
         """
 
         uniques = self.unique(self.df.columns.tolist())
-        nuniques = {}
-        for key, val in uniques.items():
-            nuniques[key] = val['count']
+        nuniques = {key: val['count'] for key, val in uniques.items()}
         return pd.Series(nuniques)
 
     def unique(self, columns: Union[str, List] = None) -> Dict[str, Any]:
diff --git a/intake_esm/merge_util.py b/intake_esm/merge_util.py
index adfa2d6c..361fbc10 100644
--- a/intake_esm/merge_util.py
+++ b/intake_esm/merge_util.py
@@ -153,10 +153,7 @@ def union(
 def _to_nested_dict(df):
     """Converts a multiindex series to nested dict"""
     if hasattr(df.index, 'levels') and len(df.index.levels) > 1:
-        ret = {}
-        for k, v in df.groupby(level=0):
-            ret[k] = _to_nested_dict(v.droplevel(0))
-        return ret
+        return {k: _to_nested_dict(v.droplevel(0)) for k, v in df.groupby(level=0)}
     return df.to_dict()
 
 
diff --git a/intake_esm/search.py b/intake_esm/search.py
index 9684dad3..4dfd77c7 100644
--- a/intake_esm/search.py
+++ b/intake_esm/search.py
@@ -15,8 +15,7 @@ def _unique(df, columns=None):
 
     def _find_unique(series):
         values = series.dropna().values
-        uniques = list(set(_flatten_list(values)))
-        return uniques
+        return list(set(_flatten_list(values)))
 
     x = df[columns].apply(_find_unique, result_type='reduce').to_dict()
     info = {}
@@ -98,7 +97,7 @@ def search(df, require_all_on=None, **query):
             if index == condition:
                 results.append(group)
 
-        if len(results) >= 1:
+        if results:
             return pd.concat(results).reset_index(drop=True)
 
         warn(message)
@@ -133,18 +132,15 @@ def _is_pattern(value):
 def _flatten_list(data):
     for item in data:
         if isinstance(item, Iterable) and not isinstance(item, str):
-            for x in _flatten_list(item):
-                yield x
+            yield from _flatten_list(item)
         else:
             yield item
 
 
 def _get_columns_with_iterables(df):
-    if not df.empty:
-        has_iterables = (
-            df.sample(20, replace=True).applymap(type).isin([list, tuple, set]).any().to_dict()
-        )
-        columns_with_iterables = [column for column, check in has_iterables.items() if check]
-    else:
-        columns_with_iterables = []
-    return columns_with_iterables
+    if df.empty:
+        return []
+    has_iterables = (
+        df.sample(20, replace=True).applymap(type).isin([list, tuple, set]).any().to_dict()
+    )
+    return [column for column, check in has_iterables.items() if check]
diff --git a/intake_esm/source.py b/intake_esm/source.py
index 2fd0c811..beb30eb4 100644
--- a/intake_esm/source.py
+++ b/intake_esm/source.py
@@ -145,8 +145,7 @@ def __init__(
 
     def __repr__(self):
         """Make string representation of object."""
-        contents = f'<name: {self.name}, assets: {len(self.df)}'
-        return contents
+        return f'<name: {self.name}, assets: {len(self.df)}'
 
     def _ipython_display_(self):
         """
@@ -254,8 +253,7 @@ def close(self):
 
 def create_nested_dict(df, path_column, aggregation_columns):
     mi = df.set_index(aggregation_columns)
-    nd = _to_nested_dict(mi[path_column])
-    return nd
+    return _to_nested_dict(mi[path_column])
 
 
 def _sanitize_aggregations(df, aggregation_dict):