Minor - Docs fixes

Author: jaidisido

awswrangler/s3/_download.py

@@ -19,7 +19,7 @@ def download(
     boto3_session: Optional[boto3.Session] = None,
     s3_additional_kwargs: Optional[Dict[str, Any]] = None,
 ) -> None:
-    """Download file from from a received S3 path to local file.
+    """Download file from a received S3 path to local file.
 
     Note
     ----

awswrangler/s3/_read_excel.py

@@ -20,7 +20,7 @@ def read_excel(
     s3_additional_kwargs: Optional[Dict[str, Any]] = None,
     **pandas_kwargs: Any,
 ) -> pd.DataFrame:
-    """Read EXCEL file(s) from from a received S3 path.
+    """Read EXCEL file(s) from a received S3 path.
 
     Note
     ----

awswrangler/s3/_read_parquet.py

@@ -565,7 +565,7 @@ def read_parquet(
     s3_additional_kwargs: Optional[Dict[str, Any]] = None,
     pyarrow_additional_kwargs: Optional[Dict[str, Any]] = None,
 ) -> Union[pd.DataFrame, Iterator[pd.DataFrame]]:
-    """Read Apache Parquet file(s) from from a received S3 prefix or list of S3 objects paths.
+    """Read Apache Parquet file(s) from a received S3 prefix or list of S3 objects paths.
 
     The concept of Dataset goes beyond the simple idea of files and enable more
     complex features like partitioning and catalog integration (AWS Glue Catalog).
@@ -998,7 +998,7 @@ def read_parquet_metadata(
     s3_additional_kwargs: Optional[Dict[str, Any]] = None,
     pyarrow_additional_kwargs: Optional[Dict[str, Any]] = None,
 ) -> Tuple[Dict[str, str], Optional[Dict[str, str]]]:
-    """Read Apache Parquet file(s) metadata from from a received S3 prefix or list of S3 objects paths.
+    """Read Apache Parquet file(s) metadata from a received S3 prefix or list of S3 objects paths.
 
     The concept of Dataset goes beyond the simple idea of files and enable more
     complex features like partitioning and catalog integration (AWS Glue Catalog).

awswrangler/s3/_read_text.py

@@ -179,7 +179,7 @@ def read_csv(
     partition_filter: Optional[Callable[[Dict[str, str]], bool]] = None,
     **pandas_kwargs: Any,
 ) -> Union[pd.DataFrame, Iterator[pd.DataFrame]]:
-    """Read CSV file(s) from from a received S3 prefix or list of S3 objects paths.
+    """Read CSV file(s) from a received S3 prefix or list of S3 objects paths.
 
     This function accepts Unix shell-style wildcards in the path argument.
     * (matches everything), ? (matches any single character),
@@ -327,7 +327,7 @@ def read_fwf(
     partition_filter: Optional[Callable[[Dict[str, str]], bool]] = None,
     **pandas_kwargs: Any,
 ) -> Union[pd.DataFrame, Iterator[pd.DataFrame]]:
-    """Read fixed-width formatted file(s) from from a received S3 prefix or list of S3 objects paths.
+    """Read fixed-width formatted file(s) from a received S3 prefix or list of S3 objects paths.
 
     This function accepts Unix shell-style wildcards in the path argument.
     * (matches everything), ? (matches any single character),
@@ -476,7 +476,7 @@ def read_json(
     partition_filter: Optional[Callable[[Dict[str, str]], bool]] = None,
     **pandas_kwargs: Any,
 ) -> Union[pd.DataFrame, Iterator[pd.DataFrame]]:
-    """Read JSON file(s) from from a received S3 prefix or list of S3 objects paths.
+    """Read JSON file(s) from a received S3 prefix or list of S3 objects paths.
 
     This function accepts Unix shell-style wildcards in the path argument.
     * (matches everything), ? (matches any single character),

awswrangler/s3/_write_parquet.py

@@ -748,11 +748,11 @@ def store_parquet_metadata(  # pylint: disable=too-many-arguments
 ) -> Tuple[Dict[str, str], Optional[Dict[str, str]], Optional[Dict[str, List[str]]]]:
     """Infer and store parquet metadata on AWS Glue Catalog.
 
-    Infer Apache Parquet file(s) metadata from from a received S3 prefix or list of S3 objects paths
+    Infer Apache Parquet file(s) metadata from a received S3 prefix
     And then stores it on AWS Glue Catalog including all inferred partitions
-    (No need of 'MSCK REPAIR TABLE')
+    (No need for 'MSCK REPAIR TABLE')
 
-    The concept of Dataset goes beyond the simple idea of files and enable more
+    The concept of Dataset goes beyond the simple idea of files and enables more
     complex features like partitioning and catalog integration (AWS Glue Catalog).
 
     This function accepts Unix shell-style wildcards in the path argument.
@@ -761,22 +761,15 @@ def store_parquet_metadata(  # pylint: disable=too-many-arguments
     If you want to use a path which includes Unix shell-style wildcard characters (`*, ?, []`),
     you can use `glob.escape(path)` before passing the path to this function.
 
-    Note
-    ----
-    On `append` mode, the `parameters` will be upsert on an existing table.
-
     Note
     ----
     In case of `use_threads=True` the number of threads
     that will be spawned will be gotten from os.cpu_count().
 
     Parameters
     ----------
-    path : Union[str, List[str]]
-        S3 prefix (accepts Unix shell-style wildcards)
-        (e.g. s3://bucket/prefix) or list of S3 objects paths (e.g. [s3://bucket/key0, s3://bucket/key1]).
-        database : str
-        Glue/Athena catalog: Database name.
+    path : str
+        S3 prefix (accepts Unix shell-style wildcards) (e.g. s3://bucket/prefix).
     table : str
         Glue/Athena catalog: Table name.
     database : str

awswrangler/s3/_write_text.py

@@ -124,11 +124,7 @@ def to_csv(  # pylint: disable=too-many-arguments,too-many-locals,too-many-state
 
     Note
     ----
-    Compression: The minimum acceptable version to achive it is Pandas 1.2.0 that requires Python >= 3.7.1.
-
-    Note
-    ----
-    On `append` mode, the `parameters` will be upsert on an existing table.
+    Compression: The minimum acceptable version to achieve it is Pandas 1.2.0 that requires Python >= 3.7.1.
 
     Note
     ----