chore: address comments from technical writers for legal review

bigframes/_config/bigquery_options.py

@@ -57,7 +57,7 @@ def application_name(self) -> Optional[str]:
         """The application name to amend to the user-agent sent to Google APIs.
 
         The application name to amend to the user agent sent to Google APIs.
-        The recommended format is  ``"appplication-name/major.minor.patch_version"``
+        The recommended format is  ``"application-name/major.minor.patch_version"``
         or ``"(gpn:PartnerName;)"`` for official Google partners.
         """
         return self._application_name

bigframes/ml/compose.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 """Build composite transformers on heterogeneous data. This module is styled
-after Scikit-Learn's compose module:
+after scikit-Learn's compose module:
 https://scikit-learn.org/stable/modules/classes.html#module-sklearn.compose."""
 
 from __future__ import annotations

bigframes/ml/imported.py

@@ -34,7 +34,7 @@ class TensorFlowModel(base.Predictor):
         model_path (str):
             GCS path that holds the model files.
         session (BigQuery Session):
-            BQ session to create the model
+            BQ session to create the model.
     """
 
     def __init__(
@@ -113,7 +113,7 @@ class ONNXModel(base.Predictor):
         model_path (str):
             Cloud Storage path that holds the model files.
         session (BigQuery Session):
-            BQ session to create the model
+            BQ session to create the model.
     """
 
     def __init__(
@@ -207,7 +207,7 @@ class XGBoostModel(base.Predictor):
             and feature_types are both specified in the model file. Supported types
             are "bool", "string", "int64", "float64", "array<bool>", "array<string>", "array<int64>", "array<float64>".
         session (BigQuery Session):
-            BQ session to create the model
+            BQ session to create the model.
     """
 
     def __init__(

third_party/bigframes_vendored/pandas/core/groupby/__init__.py

@@ -78,7 +78,7 @@ def median(
                 Include only float, int, boolean columns.
             exact (bool, default False):
                 Calculate the exact median instead of an approximation. Note:
-                    ``exact=True`` not yet supported.
+                    ``exact=True`` is not supported.
 
         Returns:
             pandas.Series or pandas.DataFrame: Median of groups.
@@ -178,7 +178,7 @@ def sum(
                 Include only float, int, boolean columns.
             min_count (int, default 0):
                 The required number of valid values to perform the operation. If fewer
-                than ``min_count`` non-NA values are present the result will be NA.
+                than ``min_count`` and non-NA values are present, the result will be NA.
 
         Returns:
             Series or DataFrame: Computed sum of values within each group.
@@ -194,7 +194,7 @@ def prod(self, numeric_only: bool = False, min_count: int = 0):
                 Include only float, int, boolean columns.
             min_count (int, default 0):
                 The required number of valid values to perform the operation. If fewer
-                than ``min_count`` non-NA values are present the result will be NA.
+                than ``min_count`` and non-NA values are present, the result will be NA.
 
         Returns:
             Series or DataFrame: Computed prod of values within each group.
@@ -214,7 +214,7 @@ def min(
                 Include only float, int, boolean columns.
             min_count (int, default 0):
                 The required number of valid values to perform the operation. If fewer
-                than ``min_count`` non-NA values are present the result will be NA.
+                than ``min_count`` and non-NA values are present, the result will be NA.
 
         Returns:
             Series or DataFrame: Computed min of values within each group.
@@ -234,7 +234,7 @@ def max(
                 Include only float, int, boolean columns.
             min_count (int, default 0):
                 The required number of valid values to perform the operation. If fewer
-                than ``min_count`` non-NA values are present the result will be NA.
+                than ``min_count`` and non-NA values are present, the result will be NA.
 
         Returns:
             Series or DataFrame: Computed max of values within each group.

third_party/bigframes_vendored/sklearn/compose/_column_transformer.py

@@ -19,9 +19,9 @@ class ColumnTransformer(_BaseComposition):
     """Applies transformers to columns of BigQuery DataFrames.
 
     This estimator allows different columns or column subsets of the input
-    to be transformed separately and the features generated by each transformer
+    to be transformed separately, and the features generated by each transformer
     will be concatenated to form a single feature space.
-    This is useful for heterogeneous or columnar data, to combine several
+    This is useful for heterogeneous or columnar data to combine several
     feature extraction mechanisms or transformations into a single transformer.
 
     Args:

third_party/bigframes_vendored/sklearn/decomposition/_pca.py

@@ -22,8 +22,8 @@ class PCA(BaseEstimator, metaclass=ABCMeta):
 
     Args:
         n_components (int, float or None, default None):
-            Number of components to keep.
-            If n_components is not set all components are kept. n_components = min(n_samples, n_features).
+            Number of components to keep. If n_components is not set all
+            components are kept, n_components = min(n_samples, n_features).
             If 0 < n_components < 1, select the number of components such that the amount of variance that needs to be explained is greater than the percentage specified by n_components.
         svd_solver ("full", "randomized" or "auto", default "auto"):
             The solver to use to calculate the principal components. Details: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-pca#pca_solver.

third_party/bigframes_vendored/sklearn/linear_model/_logistic.py

@@ -38,7 +38,7 @@ class LogisticRegression(LinearClassifierMixin, BaseEstimator):
             automatically adjust weights inversely proportional to class
             frequencies in the input data as
             ``n_samples / (n_classes * np.bincount(y))``. Dict isn't
-            supported now.
+            supported.
         l1_reg (float or None, default None):
             The amount of L1 regularization applied. Default to None. Can't be set in "normal_equation" mode. If unset, value 0 is used.
         l2_reg (float, default 0.0):

third_party/bigframes_vendored/sklearn/preprocessing/_discretization.py

@@ -18,7 +18,7 @@ class KBinsDiscretizer(TransformerMixin, BaseEstimator):
         strategy ({'uniform', 'quantile'}, default='quantile'):
             Strategy used to define the widths of the bins. 'uniform': All bins
             in each feature have identical widths. 'quantile': All bins in each
-            feature have the same number of points. Only `uniform` is supported now.
+            feature have the same number of points. Only `uniform` is supported.
     """
 
     def fit(self, X, y=None):