pandas.DataFrame insert returning self, dp.dir_dppd() returning just …

…the newly added functions

src/dppd/base.py

@@ -21,7 +21,7 @@ class register_verb:
 
     """
 
-    def __init__(self, name=None, types=None, pass_dppd=False):
+    def __init__(self, name=None, types=None, pass_dppd=False, ignore_redefine=False):
         """
         Parameters:
         -----------
@@ -43,6 +43,7 @@ def __init__(self, name=None, types=None, pass_dppd=False):
             if t not in property_registry:
                 property_registry[t] = set()
         self.pass_dppd = pass_dppd
+        self.ignore_redefine = ignore_redefine
 
     def __call__(self, func):
         if self.names is None:
@@ -76,9 +77,11 @@ def inner(*args, **kwargs):
                 if (real_name, t) in verb_registry and verb_registry[
                     (real_name, t)
                 ] != func:
-                    warnings.warn(f"redefining verb {real_name} for type {t}")
-                if t in property_registry and real_name in property_registry[t]:
-                    warnings.warn(f"verb {real_name} shadows property for type {t}")
+                    if not self.ignore_redefine:
+                        print(verb_registry.keys())
+                        warnings.warn(f"redefining verb {real_name} for type {t}")
+                    if t in property_registry and real_name in property_registry[t]:
+                        warnings.warn(f"verb {real_name} shadows property for type {t}")
 
             outer.__doc__ == func.__doc__
             for t in self.types:
@@ -167,6 +170,14 @@ def pd(self):
             self.X._self_update_wrapped(self.parent.df)
         return result
 
+    def dir_dppd(self):
+        """Return just the newly registered verbs, not the wrapped ones"""
+        total = set(dir(self))
+        old = set(dir(self.df))
+        new = total - old
+        return sorted(new)
+
+
     def __call__(self, df=None):
         if df is None:
             if self.df is None:

src/dppd/single_verbs.py

@@ -989,7 +989,7 @@ def colspec_DataFrame(df, columns, invert=False):
 
 
 @register_verb("pca", types=pd.DataFrame)
-def pca_dataframe(df, whiten=False, random_state=None):
+def pca_dataframe(df, whiten=False, random_state=None, n_components=2):
     """Perform 2 component PCA using sklearn.decomposition.PCA.
     Expects samples in rows!
     Returns a tuple (DataFrame{sample, 1st, 2nd},
@@ -998,7 +998,7 @@ def pca_dataframe(df, whiten=False, random_state=None):
     from sklearn.decomposition import PCA
     import warnings
 
-    p = PCA(n_components=2, whiten=whiten, random_state=random_state)
+    p = PCA(n_components=n_components, whiten=whiten, random_state=random_state)
     df_fit = pd.DataFrame(p.fit_transform(df))
     df_fit.columns = ["1st", "2nd"]
     df_fit.index = df.index
@@ -1008,3 +1008,11 @@ def pca_dataframe(df, whiten=False, random_state=None):
         warnings.simplefilter("ignore")
         df_fit.explained_variance_ratio_ = p.explained_variance_ratio_
     return df_fit
+
+
+@register_verb("insert", types=pd.DataFrame, ignore_redefine=True)
+def insert_return_self(df, loc, column, value, **kwargs):
+    """DataFrame.insert, but return self.
+    """
+    df.insert(loc, column, value, **kwargs)
+    return df