Reformatting with yapf

.travis.yml

@@ -25,6 +25,7 @@ matrix:
       env: LINT=1
       script:
         - export PATH="$HOME/miniconda/bin:$PATH"
+        - yapf -dr modin/pandas
         - flake8 .
 
 install:

.travis/install-dependencies.sh

@@ -55,7 +55,7 @@ elif [[ "$LINT" == "1" ]]; then
   bash miniconda.sh -b -p $HOME/miniconda
   export PATH="$HOME/miniconda/bin:$PATH"
   # Install Python linting tools.
-  pip install -q flake8 flake8-comprehensions
+  pip install -q flake8 flake8-comprehensions yapf
 else
   echo "Unrecognized environment."
   exit 1

modin/pandas/__init__.py

@@ -15,8 +15,10 @@
 
 try:
     if threading.current_thread().name == "MainThread":
-        ray.init(redirect_output=True, include_webui=False,
-                 redirect_worker_output=True)
+        ray.init(
+            redirect_output=True,
+            include_webui=False,
+            redirect_worker_output=True)
 except AssertionError:
     pass
 
@@ -41,17 +43,20 @@ def get_npartitions():
 from .concat import concat  # noqa: 402
 from .dataframe import DataFrame  # noqa: 402
 from .datetimes import to_datetime  # noqa: 402
-from .io import (read_csv, read_parquet, read_json, read_html,  # noqa: 402
-                 read_clipboard, read_excel, read_hdf, read_feather,  # noqa: 402
-                 read_msgpack, read_stata, read_sas, read_pickle,  # noqa: 402
-                 read_sql)  # noqa: 402
+from .io import (  # noqa: 402
+    read_csv, read_parquet, read_json, read_html, read_clipboard, read_excel,
+    read_hdf, read_feather, read_msgpack, read_stata, read_sas, read_pickle,
+    read_sql)
 from .reshape import get_dummies  # noqa: 402
 
 __all__ = [
-    "DataFrame", "Series", "read_csv", "read_parquet", "concat", "eval",
-    "unique", "value_counts", "cut", "to_numeric", "factorize", "test", "qcut",
-    "match", "to_datetime", "get_dummies", "Panel", "date_range", "Index",
-    "MultiIndex", "Series", "bdate_range", "DatetimeIndex", "to_timedelta",
-    "set_eng_float_format", "set_option", "CategoricalIndex", "Timedelta",
-    "Timestamp", "NaT", "PeriodIndex", "Categorical"
+    "DataFrame", "Series", "read_csv", "read_parquet", "read_json",
+    "read_html", "read_clipboard", "read_excel", "read_hdf", "read_feather",
+    "read_msgpack", "read_stata", "read_sas", "read_pickle", "read_sql",
+    "concat", "eval", "unique", "value_counts", "cut", "to_numeric",
+    "factorize", "test", "qcut", "match", "to_datetime", "get_dummies",
+    "Panel", "date_range", "Index", "MultiIndex", "Series", "bdate_range",
+    "DatetimeIndex", "to_timedelta", "set_eng_float_format", "set_option",
+    "CategoricalIndex", "Timedelta", "Timestamp", "NaT", "PeriodIndex",
+    "Categorical"
 ]

modin/pandas/concat.py

@@ -10,8 +10,15 @@
 from .utils import _reindex_helper
 
 
-def concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False,
-           keys=None, levels=None, names=None, verify_integrity=False,
+def concat(objs,
+           axis=0,
+           join='outer',
+           join_axes=None,
+           ignore_index=False,
+           keys=None,
+           levels=None,
+           names=None,
+           verify_integrity=False,
            copy=True):
 
     if keys is not None:
@@ -28,24 +35,24 @@ def concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False,
         raise ValueError("All objects passed were None")
 
     try:
-        type_check = next(obj for obj in objs
-                          if not isinstance(obj, (pandas.Series,
-                                                  pandas.DataFrame,
-                                                  DataFrame)))
+        type_check = next(
+            obj for obj in objs
+            if not isinstance(obj, (pandas.Series, pandas.DataFrame,
+                                    DataFrame)))
     except StopIteration:
         type_check = None
     if type_check is not None:
-        raise ValueError("cannot concatenate object of type \"{0}\"; only "
-                         "pandas.Series, pandas.DataFrame, "
-                         "and modin.pandas.DataFrame objs are "
-                         "valid", type(type_check))
+        raise ValueError(
+            "cannot concatenate object of type \"{0}\"; only "
+            "pandas.Series, pandas.DataFrame, "
+            "and modin.pandas.DataFrame objs are "
+            "valid", type(type_check))
 
-    all_series = all(isinstance(obj, pandas.Series)
-                     for obj in objs)
+    all_series = all(isinstance(obj, pandas.Series) for obj in objs)
     if all_series:
-        return DataFrame(pandas.concat(objs, axis, join, join_axes,
-                                       ignore_index, keys, levels, names,
-                                       verify_integrity, copy))
+        return DataFrame(
+            pandas.concat(objs, axis, join, join_axes, ignore_index, keys,
+                          levels, names, verify_integrity, copy))
 
     if isinstance(objs, dict):
         raise NotImplementedError(
@@ -59,8 +66,8 @@ def concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False,
                          " other axis")
 
     # We need this in a list because we use it later.
-    all_index, all_columns = list(zip(*[(obj.index, obj.columns)
-                                        for obj in objs]))
+    all_index, all_columns = list(
+        zip(*[(obj.index, obj.columns) for obj in objs]))
 
     def series_to_df(series, columns):
         df = pandas.DataFrame(series)
@@ -71,8 +78,10 @@ def series_to_df(series, columns):
     # true regardless of the existence of another column named 0 in the
     # concat.
     if axis == 0:
-        objs = [series_to_df(obj, [0])
-                if isinstance(obj, pandas.Series) else obj for obj in objs]
+        objs = [
+            series_to_df(obj, [0]) if isinstance(obj, pandas.Series) else obj
+            for obj in objs
+        ]
     else:
         # Pandas starts the count at 0 so this will increment the names as
         # long as there's a new nameless Series being added.
@@ -82,9 +91,11 @@ def name_incrementer(i):
             return val
 
         i = [0]
-        objs = [series_to_df(obj, obj.name if obj.name is not None
-                             else name_incrementer(i))
-                if isinstance(obj, pandas.Series) else obj for obj in objs]
+        objs = [
+            series_to_df(
+                obj, obj.name if obj.name is not None else name_incrementer(i))
+            if isinstance(obj, pandas.Series) else obj for obj in objs
+        ]
 
     # Using concat on the columns and index is fast because they're empty,
     # and it forces the error checking. It also puts the columns in the
@@ -105,31 +116,38 @@ def name_incrementer(i):
 
     # Put all of the DataFrames into Ray format
     # TODO just partition the DataFrames instead of building a new Ray DF.
-    objs = [DataFrame(obj) if isinstance(obj, (pandas.DataFrame,
-                                               pandas.Series)) else obj
-            for obj in objs]
+    objs = [
+        DataFrame(obj)
+        if isinstance(obj, (pandas.DataFrame, pandas.Series)) else obj
+        for obj in objs
+    ]
 
     # Here we reuse all_columns/index so we don't have to materialize objects
     # from remote memory built in the previous line. In the future, we won't be
     # building new DataFrames, rather just partitioning the DataFrames.
     if axis == 0:
-        new_blocks = np.array([_reindex_helper._submit(
-            args=tuple([all_columns[i], final_columns, axis,
-                       len(objs[0]._block_partitions)] + part.tolist()),
-            num_return_vals=len(objs[0]._block_partitions))
-            for i in range(len(objs))
-            for part in objs[i]._block_partitions])
+        new_blocks = np.array([
+            _reindex_helper._submit(
+                args=tuple([
+                    all_columns[i], final_columns, axis,
+                    len(objs[0]._block_partitions)
+                ] + part.tolist()),
+                num_return_vals=len(objs[0]._block_partitions))
+            for i in range(len(objs)) for part in objs[i]._block_partitions
+        ])
     else:
         # Transposing the columns is necessary because the remote task treats
         # everything like rows and returns in row-major format. Luckily, this
         # operation is cheap in numpy.
-        new_blocks = np.array([_reindex_helper._submit(
-            args=tuple([all_index[i], final_index, axis,
-                       len(objs[0]._block_partitions.T)] + part.tolist()),
-            num_return_vals=len(objs[0]._block_partitions.T))
-            for i in range(len(objs))
-            for part in objs[i]._block_partitions.T]).T
-
-    return DataFrame(block_partitions=new_blocks,
-                     columns=final_columns,
-                     index=final_index)
+        new_blocks = np.array([
+            _reindex_helper._submit(
+                args=tuple([
+                    all_index[i], final_index, axis,
+                    len(objs[0]._block_partitions.T)
+                ] + part.tolist()),
+                num_return_vals=len(objs[0]._block_partitions.T))
+            for i in range(len(objs)) for part in objs[i]._block_partitions.T
+        ]).T
+
+    return DataFrame(
+        block_partitions=new_blocks, columns=final_columns, index=final_index)