Improve readibility

optimus/engines/cudf/io/load.py

@@ -3,10 +3,11 @@
 import cudf
 
 from optimus.engines.base.io.load import BaseLoad
+from optimus.engines.base.meta import Meta
+from optimus.engines.cudf.dataframe import CUDFDataFrame
 from optimus.helpers.functions import prepare_path
 from optimus.helpers.logger import logger
-from optimus.engines.cudf.dataframe import CUDFDataFrame
-from optimus.engines.base.meta import Meta
+
 
 class Load(BaseLoad):
 
@@ -87,9 +88,10 @@ def csv(path, sep=',', header=True, infer_schema=True, encoding="utf-8", null_va
                 dtype = ["str"]
 
             cdf = cudf.read_csv(path, sep=sep, header=header, encoding=encoding,
-                               quoting=quoting, error_bad_lines=error_bad_lines,
-                               keep_default_na=keep_default_na, na_values=null_value, nrows=n_rows, na_filter=na_filter,
-                               dtype=dtype)
+                                quoting=quoting, error_bad_lines=error_bad_lines,
+                                keep_default_na=keep_default_na, na_values=null_value, nrows=n_rows,
+                                na_filter=na_filter,
+                                dtype=dtype)
             df = CUDFDataFrame(cdf)
             df.meta = Meta.set(df.meta, "file_name", path)
         except IOError as error:

optimus/engines/dask/io/load.py

@@ -6,11 +6,11 @@
 
 import optimus.helpers.functions_spark
 from optimus.engines.base.io.load import BaseLoad
+from optimus.engines.base.meta import Meta
+from optimus.engines.dask.dataframe import DaskDataFrame
 from optimus.helpers.core import val_to_list
 from optimus.helpers.functions import prepare_path
 from optimus.helpers.logger import logger
-from optimus.engines.dask.dataframe import DaskDataFrame
-from optimus.engines.base.meta import Meta
 
 
 class Load(BaseLoad):
@@ -84,9 +84,9 @@ def csv(path, sep=',', header=True, infer_schema=True, na_values=None, encoding=
             # Detect missing value markers (empty strings and the value of na_values). In data without any NAs,
             # passing na_filter=False can improve the performance of reading a large file.
             ddf = dd.read_csv(path, sep=sep, header=0 if header else None, encoding=encoding,
-                             quoting=quoting, lineterminator=lineterminator, error_bad_lines=error_bad_lines,
-                             keep_default_na=True, na_values=None, engine=engine, na_filter=na_filter, *args,
-                             **kwargs)
+                              quoting=quoting, lineterminator=lineterminator, error_bad_lines=error_bad_lines,
+                              keep_default_na=True, na_values=None, engine=engine, na_filter=na_filter, *args,
+                              **kwargs)
 
             if n_rows > -1:
                 ddf = dd.from_pandas(ddf.head(n_rows), npartitions=1)
@@ -199,7 +199,7 @@ def avro(path, *args, **kwargs):
         file, file_name = prepare_path(path, "avro")
 
         try:
-            df = db.read_avro(path, *args, **kwargs).to_dataframe()
+            df = dd.read_avro(path, *args, **kwargs).to_dataframe()
             df.meta = Meta.set(df.meta, "file_name", file_name)
 
         except IOError as error:

optimus/engines/ibis/functions.py

@@ -10,7 +10,6 @@
 
 
 class IbisFunctions(Functions):
-
     def _to_float(self, series, *args):
         return series.cast("float64")
 

optimus/engines/spark/columns.py

@@ -168,7 +168,7 @@ def copy(self, input_cols, output_cols=None, columns=None):
             dfd = df.data.withColumn(output_col, F.col(input_col))
             meta.set(value=current_meta)
             meta = meta.copy({input_col: output_col})
-        
+
         return self.root.new(dfd, meta=meta)
 
     @staticmethod
@@ -403,7 +403,7 @@ def _cast_to(col_name, attr):
         for input_col, output_col, data_type in zip(input_cols, output_cols, _dtype):
             return_type, func, func_type = cast_factory(data_type)
             dfd = self.apply(input_col, func, func_return_type=return_type, args=data_type, func_type=func_type,
-                            output_cols=output_col, meta_action=Actions.CAST.value)
+                             output_cols=output_col, meta_action=Actions.CAST.value)
 
         return dfd
 
@@ -897,7 +897,8 @@ def _match(_input_cols, attr):
             _search = attr[0]
             return F.when(F.col(_input_cols).rlike(_search), True).otherwise(False)
 
-        return self.apply(input_cols, func=_match, args=(regex,), func_type="column_expr", func_return_type=str, output_cols=output_cols,
+        return self.apply(input_cols, func=_match, args=(regex,), func_type="column_expr", func_return_type=str,
+                          output_cols=output_cols,
                           meta_action=Actions.REPLACE_REGEX.value)
 
     def replace(self, input_cols, search=None, replace_by=None, search_by="chars", output_cols=None):
@@ -1412,7 +1413,8 @@ def _unnest(row):
 
             else:
                 RaiseIt.type_error(input_col, ["string", "struct", "array", "vector"])
-            df.meta = Meta.set(df.meta, value=df.meta.preserve(None, Actions.UNNEST.value, [v for k, v in final_columns]).get())
+            df.meta = Meta.set(df.meta,
+                               value=df.meta.preserve(None, Actions.UNNEST.value, [v for k, v in final_columns]).get())
         return df
 
     @staticmethod