edits (#109)

verticapy/learn/vmodel.py

@@ -5115,7 +5115,7 @@ def plot(
     ):
         """
     ---------------------------------------------------------------------------
-    Draws the Decomposition scatter plot.
+    Draws a decomposition scatter plot.
 
     Parameters
     ----------
@@ -5145,7 +5145,7 @@ def plot_circle(
     ):
         """
     ---------------------------------------------------------------------------
-    Draws the Decomposition Circle.
+    Draws a decomposition circle.
 
     Parameters
     ----------

verticapy/stats/tools.py

@@ -334,30 +334,31 @@ def cochrane_orcutt(
 ):
     """
 ---------------------------------------------------------------------------
-Cochrane Orcutt.
+Performs a Cochrane-Orcutt estimation.
 
 Parameters
 ----------
 model: vModel
-    Linear Regression Object.
+    Linear regression object.
 vdf: vDataFrame / str
     Input relation.
 ts: str
-    vcolumn used as timeline. It will be to use to order the data. It can be
-    a numerical or type date like (date, datetime, timestamp...) vcolumn.
+    vcolumn of numeric or date-like type (date, datetime, timestamp, etc.)
+    used as the timeline and to order the data.
 prais_winsten: bool, optional
-    If set to True, the first term is not lost which leads to more precision.
-    This configuration is called the Prais–Winsten estimation.
+    If true, retains the first observation of the time series, increasing
+    precision and efficiency. This configuration is called the 
+    Prais–Winsten estimation.
 drop_tmp_model: bool, optional
-    If set to True, it drops the temporary model.
+    If true, it drops the temporary model.
 
 Returns
 -------
 model
     A Linear Model with the different information stored as attributes:
      - coef_        : Model's coefficients.
-     - pho_         : Cochrane Orcutt pho.
-     - anova_table_ : Anova Table.
+     - pho_         : Cochrane-Orcutt pho.
+     - anova_table_ : ANOVA table.
      - r2_          : R2
     """
     check_types(

verticapy/utilities.py

@@ -460,9 +460,9 @@ def pandas_to_vertica(
     """
 ---------------------------------------------------------------------------
 Ingests a pandas DataFrame into the Vertica database by creating a CSV file 
-and then using flex tables. It will create a temporary local table and it
-will be dropped at the end of the local session.
-Use vDataFrame.to_db to store it inside the Database.
+and then using flex tables. This creates a temporary local table that
+will be dropped at the end of the local session. Use vDataFrame.to_db 
+to store it inside the database.
 
 Parameters
 ----------
@@ -710,12 +710,12 @@ def read_csv(
 insert: bool, optional
 	If set to True, the data will be ingested to the input relation. Be sure
 	that your file has a header corresponding to the name of the relation
-	columns otherwise the ingestion will not work.
+	columns, otherwise ingestion will fail.
 temporary_table: bool, optional
     If set to True, a temporary table will be created.
 temporary_local_table: bool, optional
     If set to True, a temporary local table will be created. The parameter 'schema'
-    has to be empty, otherwise this parameter is ignored.
+    must to be empty, otherwise this parameter is ignored.
 
 Returns
 -------
@@ -908,8 +908,8 @@ def read_json(
 temporary_table: bool, optional
     If set to True, a temporary table will be created.
 temporary_local_table: bool, optional
-    If set to True, a temporary local table will be created. The parameter 'schema'
-    is then ignored.
+    If set to True, a temporary local table will be created and the parameter
+    'schema' is ignored.
 
 Returns
 -------

verticapy/vdataframe.py

@@ -3264,20 +3264,20 @@ def balance(
     ---------------------------------------------------------------------------
     Balances the dataset using the input method.
 
-    \u26A0 Warning : The result may be inconsistent between attempts at SQL
-                     code generation if the data are not sorted.
+    \u26A0 Warning : If the data is not sorted, the generated SQL code may
+                     differ between attempts.
 
     Parameters
     ----------
     column: str
         Column used to compute the different categories.
     method: str, optional
-        The Sample method.
-            hybrid : hybrid sampling.
-            over   : over sampling.
-            under  : under sampling.
+        The method with which to sample the data
+            hybrid : hybrid sampling
+            over   : oversampling
+            under  : undersampling
     x: float, optional
-        The desired ratio between the majority class and the minority classes.
+        The desired ratio between the majority class and minority classes.
         Only used when method is 'over' or 'under'.
     order_by: list, optional
         vColumns used to sort the data.
@@ -3552,26 +3552,25 @@ def cdt(self,
             tcdt: bool = True):
         """
     ---------------------------------------------------------------------------
-    Returns the Completely Disjonctive Table of the vDataFrame.
-    The numerical features are transformed to categorical ones using
+    Returns the complete disjunctive table of the vDataFrame.
+    Numerical features are transformed to categorical using
     the 'discretize' method. Applying PCA on TCDT leads to MCA 
     (Multiple correspondence analysis).
 
     \u26A0 Warning : This method can become computationally expensive when
-                     dealing with categorical variables having too many
-                     categories.
+                     used with categorical variables with many categories.
 
     Parameters
     ----------
     columns: list, optional
         List of the vColumns names.
     max_cardinality: int, optional
-        For any categorical variable, keeps the 'max_cardinality' most frequent 
-        categories and merge the other into one unique category.
+        For any categorical variable, keeps the most frequent categories and 
+        merges the less frequent categories into a new unique category.
     nbins: int, optional
         Number of bins used for the discretization (must be > 1).
     tcdt: bool, optional
-        If set to True, returns the Transformed Completely Disjonctive Table 
+        If set to True, returns the transformed complete disjunctive table 
         (TCDT). 
 
     Returns