Merge in formatting changes from master

.git-blame-ignore-revs

@@ -0,0 +1,4 @@
+# .git-blame-ignore-revs
+# Re-formatted entire code base with black
+7ebf0753485c931db4135953dcd0864b4d089ed5
+

.github/pull_request_template.md

@@ -3,6 +3,7 @@
 
 ### Checklist
 - [ ] Wrote a description of my changes above 
+- [ ] Formatted my code with [`black`](https://black.readthedocs.io/en/stable/index.html)
 - [ ] Added a bullet point for my changes to the top of the `CHANGELOG.md` file
 - [ ] Added or modified unit tests to reflect my changes
 - [ ] Manually tested with a notebook

.github/workflows/lint.yaml

@@ -0,0 +1,10 @@
+name: Lint
+
+on: [push, pull_request]
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - uses: psf/black@stable

CHANGELOG.md

@@ -5,6 +5,8 @@
 ### Updates
 
 * Officially drop support for Python 3.6. Sparkmagic will not guarantee Python 3.6 compatibility moving forward.
+* Re-format all code with [`black`](https://black.readthedocs.io/en/stable/index.html) and validate via CI
+
 
 ### Bug Fixes
 

README.md

@@ -1,4 +1,5 @@
-[![Build Status](https://travis-ci.org/jupyter-incubator/sparkmagic.svg?branch=master)](https://travis-ci.org/jupyter-incubator/sparkmagic) [![Join the chat at https://gitter.im/sparkmagic/Lobby](https://badges.gitter.im/sparkmagic/Lobby.svg)](https://gitter.im/sparkmagic/Lobby?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
+[![Build Status](https://travis-ci.org/jupyter-incubator/sparkmagic.svg?branch=master)](https://travis-ci.org/jupyter-incubator/sparkmagic) [![Join the chat at https://gitter.im/sparkmagic/Lobby](https://badges.gitter.im/sparkmagic/Lobby.svg)](https://gitter.im/sparkmagic/Lobby?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
+
 
 # sparkmagic
 

autovizwidget/autovizwidget/plotlygraphs/bargraph.py

@@ -10,4 +10,3 @@ class BarGraph(GraphBase):
     def _get_data(self, df, encoding):
         x_values, y_values = GraphBase._get_x_y_values(df, encoding)
         return [Bar(x=x_values, y=y_values)]
-

autovizwidget/autovizwidget/plotlygraphs/datagraph.py

@@ -8,6 +8,7 @@
 class DataGraph(object):
     """This does not use the table version of plotly because it freezes up the browser for >60 rows. Instead, we use
     pandas df HTML representation."""
+
     def __init__(self, display=None):
         if display is None:
             self.display = IpythonDisplay()
@@ -21,7 +22,8 @@ def render(self, df, encoding, output):
             show_dimensions = pd.get_option("display.show_dimensions")
 
             # This will hide the index column for pandas df.
-            self.display.html("""
+            self.display.html(
+                """
 <style>
     table.dataframe.hideme thead th:first-child {
         display: none;
@@ -30,9 +32,17 @@ def render(self, df, encoding, output):
         display: none;
     }
 </style>
-""")
-            self.display.html(df.to_html(max_rows=max_rows, max_cols=max_cols,
-                                         show_dimensions=show_dimensions, notebook=True, classes="hideme"))
+"""
+            )
+            self.display.html(
+                df.to_html(
+                    max_rows=max_rows,
+                    max_cols=max_cols,
+                    show_dimensions=show_dimensions,
+                    notebook=True,
+                    classes="hideme",
+                )
+            )
 
     @staticmethod
     def display_logarithmic_x_axis():

autovizwidget/autovizwidget/plotlygraphs/graphbase.py

@@ -3,6 +3,7 @@
 
 from plotly.graph_objs import Figure, Layout
 from plotly.offline import iplot
+
 try:
     from pandas.core.base import DataError
 except:
@@ -29,16 +30,20 @@ def render(self, df, encoding, output):
         type_x_axis = self._get_type_axis(encoding.logarithmic_x_axis)
         type_y_axis = self._get_type_axis(encoding.logarithmic_y_axis)
 
-        layout = Layout(xaxis=dict(type=type_x_axis, rangemode="tozero", title=encoding.x),
-                        yaxis=dict(type=type_y_axis, rangemode="tozero", title=encoding.y))
+        layout = Layout(
+            xaxis=dict(type=type_x_axis, rangemode="tozero", title=encoding.x),
+            yaxis=dict(type=type_y_axis, rangemode="tozero", title=encoding.y),
+        )
 
         with output:
             try:
                 fig = Figure(data=data, layout=layout)
                 iplot(fig, show_link=False)
             except TypeError:
-                print("\n\n\nPlease select another set of X and Y axis, because the type of the current axis do\n"
-                      "not support aggregation over it.")
+                print(
+                    "\n\n\nPlease select another set of X and Y axis, because the type of the current axis do\n"
+                    "not support aggregation over it."
+                )
 
     @staticmethod
     def display_x():
@@ -68,10 +73,9 @@ def _get_data(self, df, encoding):
     @staticmethod
     def _get_x_y_values(df, encoding):
         try:
-            x_values, y_values = GraphBase._get_x_y_values_aggregated(df,
-                                                                      encoding.x,
-                                                                      encoding.y,
-                                                                      encoding.y_aggregation)
+            x_values, y_values = GraphBase._get_x_y_values_aggregated(
+                df, encoding.x, encoding.y, encoding.y_aggregation
+            )
         except ValueError:
             x_values = GraphBase._get_x_values(df, encoding)
             y_values = GraphBase._get_y_values(df, encoding)
@@ -99,8 +103,11 @@ def _get_x_y_values_aggregated(df, x_column, y_column, y_aggregation):
         try:
             df_grouped = df.groupby(x_column)
         except TypeError:
-            raise InvalidEncodingError("Cannot group by X column '{}' because of its type: '{}'."
-                                       .format(df[x_column].dtype))
+            raise InvalidEncodingError(
+                "Cannot group by X column '{}' because of its type: '{}'.".format(
+                    df[x_column].dtype
+                )
+            )
         else:
             try:
                 if y_aggregation == Encoding.y_agg_avg:
@@ -114,20 +121,30 @@ def _get_x_y_values_aggregated(df, x_column, y_column, y_aggregation):
                 elif y_aggregation == Encoding.y_agg_count:
                     df_transformed = df_grouped.count()
                 else:
-                    raise ValueError("Y aggregation '{}' not supported.".format(y_aggregation))
+                    raise ValueError(
+                        "Y aggregation '{}' not supported.".format(y_aggregation)
+                    )
             except (DataError, ValueError) as err:
-                raise InvalidEncodingError("Cannot aggregate column '{}' with aggregation function '{}' because:\n\t'{}'."
-                                           .format(y_column, y_aggregation, err))
+                raise InvalidEncodingError(
+                    "Cannot aggregate column '{}' with aggregation function '{}' because:\n\t'{}'.".format(
+                        y_column, y_aggregation, err
+                    )
+                )
             except TypeError:
-                raise InvalidEncodingError("Cannot aggregate column '{}' with aggregation function '{}' because the type\n"
-                                           "cannot be aggregated over."
-                                           .format(y_column, y_aggregation))
+                raise InvalidEncodingError(
+                    "Cannot aggregate column '{}' with aggregation function '{}' because the type\n"
+                    "cannot be aggregated over.".format(y_column, y_aggregation)
+                )
             else:
                 df_transformed = df_transformed.reset_index()
 
                 if y_column not in df_transformed.columns:
-                    raise InvalidEncodingError("Y column '{}' is not valid with aggregation function '{}'. Please select "
-                                               "a different\naggregation function.".format(y_column, y_aggregation))
+                    raise InvalidEncodingError(
+                        "Y column '{}' is not valid with aggregation function '{}'. Please select "
+                        "a different\naggregation function.".format(
+                            y_column, y_aggregation
+                        )
+                    )
 
                 x_values = df_transformed[x_column].tolist()
                 y_values = df_transformed[y_column].tolist()

autovizwidget/autovizwidget/plotlygraphs/graphrenderer.py

@@ -14,7 +14,6 @@
 
 
 class GraphRenderer(object):
-
     @staticmethod
     def render(df, encoding, output):
         with output:

autovizwidget/autovizwidget/plotlygraphs/linegraph.py

@@ -7,7 +7,6 @@
 
 
 class LineGraph(GraphBase):
-
     def _get_data(self, df, encoding):
         x_values, y_values = GraphBase._get_x_y_values(df, encoding)
         return [Scatter(x=x_values, y=y_values)]

autovizwidget/autovizwidget/plotlygraphs/piegraph.py

@@ -3,11 +3,12 @@
 
 from plotly.graph_objs import Pie, Figure
 from plotly.offline import iplot
+
 try:
     from pandas.core.base import DataError
 except:
     from pandas.core.groupby import DataError
-    
+
 import autovizwidget.utils.configuration as conf
 from .graphbase import GraphBase
 
@@ -24,13 +25,19 @@ def render(df, encoding, output):
             values, labels = PieGraph._get_x_values_labels(df, encoding)
         except TypeError:
             with output:
-                print("\n\n\nCannot group by X selection because of its type: '{}'. Please select another column."
-                      .format(df[encoding.x].dtype))
+                print(
+                    "\n\n\nCannot group by X selection because of its type: '{}'. Please select another column.".format(
+                        df[encoding.x].dtype
+                    )
+                )
                 return
         except (ValueError, DataError):
             with output:
-                print("\n\n\nCannot group by X selection. Please select another column."
-                      .format(df[encoding.x].dtype))
+                print(
+                    "\n\n\nCannot group by X selection. Please select another column.".format(
+                        df[encoding.x].dtype
+                    )
+                )
                 if df.size == 0:
                     print("\n\n\nCannot display a pie graph for an empty data set.")
                 return
@@ -42,9 +49,12 @@ def render(df, encoding, output):
             # 500 rows take ~15 s.
             # 100 rows is almost automatic.
             if len(values) > max_slices_pie_graph:
-                print("There's {} values in your pie graph, which would render the graph unresponsive.\n"
-                      "Please select another X with at most {} possible values."
-                      .format(len(values), max_slices_pie_graph))
+                print(
+                    "There's {} values in your pie graph, which would render the graph unresponsive.\n"
+                    "Please select another X with at most {} possible values.".format(
+                        len(values), max_slices_pie_graph
+                    )
+                )
             else:
                 data = [Pie(values=values, labels=labels)]
 

autovizwidget/autovizwidget/plotlygraphs/scattergraph.py

@@ -4,7 +4,6 @@
 
 
 class ScatterGraph(GraphBase):
-
     def _get_data(self, df, encoding):
         x_values, y_values = GraphBase._get_x_y_values(df, encoding)
-        return [Scatter(x=x_values, y=y_values, mode='markers')]
+        return [Scatter(x=x_values, y=y_values, mode="markers")]