[missing values] Reverting replacing missing values with zeros

superset/assets/backendSync.json

@@ -3725,4 +3725,5 @@
       "default": false
     }
   }
-}
+}
+

superset/assets/src/explore/controls.jsx

@@ -1350,7 +1350,7 @@ export const controls = {
       'mean',
       'min',
       'max',
-      'stdev',
+      'std',
       'var',
     ]),
     default: 'sum',

superset/common/query_context.py

@@ -41,7 +41,6 @@ class QueryContext:
     to retrieve the data payload for a given viz.
     """
 
-    default_fillna = 0
     cache_type = 'df'
     enforce_numerical_metrics = True
 
@@ -103,7 +102,6 @@ def get_query_result(self, query_object):
                 self.df_metrics_to_num(df, query_object)
 
             df.replace([np.inf, -np.inf], np.nan)
-            df = self.handle_nulls(df)
         return {
             'query': result.query,
             'status': result.status,
@@ -118,27 +116,6 @@ def df_metrics_to_num(self, df, query_object):
             if dtype.type == np.object_ and col in metrics:
                 df[col] = pd.to_numeric(df[col], errors='coerce')
 
-    def handle_nulls(self, df):
-        fillna = self.get_fillna_for_columns(df.columns)
-        return df.fillna(fillna)
-
-    def get_fillna_for_col(self, col):
-        """Returns the value to use as filler for a specific Column.type"""
-        if col and col.is_string:
-            return ' NULL'
-        return self.default_fillna
-
-    def get_fillna_for_columns(self, columns=None):
-        """Returns a dict or scalar that can be passed to DataFrame.fillna"""
-        if columns is None:
-            return self.default_fillna
-        columns_dict = {col.column_name: col for col in self.datasource.columns}
-        fillna = {
-            c: self.get_fillna_for_col(columns_dict.get(c))
-            for c in columns
-        }
-        return fillna
-
     def get_data(self, df):
         return df.to_dict(orient='records')
 

superset/viz.py

@@ -75,7 +75,6 @@ class BaseViz(object):
     verbose_name = 'Base Viz'
     credits = ''
     is_timeseries = False
-    default_fillna = 0
     cache_type = 'df'
     enforce_numerical_metrics = True
 
@@ -164,28 +163,6 @@ def run_extra_queries(self):
         """
         pass
 
-    def handle_nulls(self, df):
-        fillna = self.get_fillna_for_columns(df.columns)
-        return df.fillna(fillna)
-
-    def get_fillna_for_col(self, col):
-        """Returns the value to use as filler for a specific Column.type"""
-        if col:
-            if col.is_string:
-                return ' NULL'
-        return self.default_fillna
-
-    def get_fillna_for_columns(self, columns=None):
-        """Returns a dict or scalar that can be passed to DataFrame.fillna"""
-        if columns is None:
-            return self.default_fillna
-        columns_dict = {col.column_name: col for col in self.datasource.columns}
-        fillna = {
-            c: self.get_fillna_for_col(columns_dict.get(c))
-            for c in columns
-        }
-        return fillna
-
     def get_samples(self):
         query_obj = self.query_obj()
         query_obj.update({
@@ -254,8 +231,7 @@ def get_df(self, query_obj=None):
             if self.enforce_numerical_metrics:
                 self.df_metrics_to_num(df)
 
-            df.replace([np.inf, -np.inf], np.nan)
-            df = self.handle_nulls(df)
+            df.replace([np.inf, -np.inf], np.nan, inplace=True)
         return df
 
     def df_metrics_to_num(self, df):
@@ -653,7 +629,9 @@ def get_data(self, df):
         pt = df.pivot_table(
             index=DTTM_ALIAS,
             columns=columns,
-            values=values)
+            values=values,
+            dropna=False,
+        )
         pt.index = pt.index.map(str)
         pt = pt.sort_index()
         return dict(
@@ -696,20 +674,28 @@ def get_data(self, df):
                 self.form_data.get('granularity') == 'all' and
                 DTTM_ALIAS in df):
             del df[DTTM_ALIAS]
+
+        aggfunc = self.form_data.get('pandas_aggfunc')
+
+        # Ensure that Pandas's sum function mimics that of SQL.
+        if aggfunc == 'sum':
+            aggfunc = lambda x: x.sum(min_count=1)  # noqa: E731
+
         df = df.pivot_table(
             index=self.form_data.get('groupby'),
             columns=self.form_data.get('columns'),
             values=[utils.get_metric_name(m) for m in self.form_data.get('metrics')],
-            aggfunc=self.form_data.get('pandas_aggfunc'),
+            aggfunc=aggfunc,
             margins=self.form_data.get('pivot_margins'),
+            dropna=False,
         )
         # Display metrics side by side with each column
         if self.form_data.get('combine_metric'):
             df = df.stack(0).unstack()
         return dict(
             columns=list(df.columns),
             html=df.to_html(
-                na_rep='',
+                na_rep='null',
                 classes=(
                     'dataframe table table-striped table-bordered '
                     'table-condensed table-hover').split(' ')),
@@ -877,7 +863,7 @@ def to_series(self, df, classed='', title_suffix=''):
                 index_value = label_sep.join(index_value)
             boxes = defaultdict(dict)
             for (label, key), value in row.items():
-                if key == 'median':
+                if key == 'nanmedian':
                     key = 'Q2'
                 boxes[label][key] = value
             for label, box in boxes.items():
@@ -894,28 +880,24 @@ def to_series(self, df, classed='', title_suffix=''):
 
     def get_data(self, df):
         form_data = self.form_data
-        df = df.fillna(0)
 
         # conform to NVD3 names
         def Q1(series):  # need to be named functions - can't use lambdas
-            return np.percentile(series, 25)
+            return np.nanpercentile(series, 25)
 
         def Q3(series):
-            return np.percentile(series, 75)
+            return np.nanpercentile(series, 75)
 
         whisker_type = form_data.get('whisker_options')
         if whisker_type == 'Tukey':
 
             def whisker_high(series):
                 upper_outer_lim = Q3(series) + 1.5 * (Q3(series) - Q1(series))
-                series = series[series <= upper_outer_lim]
-                return series[np.abs(series - upper_outer_lim).argmin()]
+                return series[series <= upper_outer_lim].max()
 
             def whisker_low(series):
                 lower_outer_lim = Q1(series) - 1.5 * (Q3(series) - Q1(series))
-                # find the closest value above the lower outer limit
-                series = series[series >= lower_outer_lim]
-                return series[np.abs(series - lower_outer_lim).argmin()]
+                return series[series >= lower_outer_lim].min()
 
         elif whisker_type == 'Min/max (no outliers)':
 
@@ -929,10 +911,10 @@ def whisker_low(series):
             low, high = whisker_type.replace(' percentiles', '').split('/')
 
             def whisker_high(series):
-                return np.percentile(series, int(high))
+                return np.nanpercentile(series, int(high))
 
             def whisker_low(series):
-                return np.percentile(series, int(low))
+                return np.nanpercentile(series, int(low))
 
         else:
             raise ValueError('Unknown whisker type: {}'.format(whisker_type))
@@ -943,7 +925,7 @@ def outliers(series):
             # pandas sometimes doesn't like getting lists back here
             return set(above.tolist() + below.tolist())
 
-        aggregate = [Q1, np.median, Q3, whisker_high, whisker_low, outliers]
+        aggregate = [Q1, np.nanmedian, Q3, whisker_high, whisker_low, outliers]
         df = df.groupby(form_data.get('groupby')).agg(aggregate)
         chart_data = self.to_series(df)
         return chart_data
@@ -1034,7 +1016,6 @@ def as_floats(field):
         return d
 
     def get_data(self, df):
-        df = df.fillna(0)
         df['metric'] = df[[utils.get_metric_name(self.metric)]]
         values = df['metric'].values
         return {
@@ -1152,22 +1133,25 @@ def to_series(self, df, classed='', title_suffix=''):
 
     def process_data(self, df, aggregate=False):
         fd = self.form_data
-        df = df.fillna(0)
         if fd.get('granularity') == 'all':
             raise Exception(_('Pick a time granularity for your time series'))
 
         if not aggregate:
             df = df.pivot_table(
                 index=DTTM_ALIAS,
                 columns=fd.get('groupby'),
-                values=self.metric_labels)
+                values=self.metric_labels,
+                dropna=False,
+            )
         else:
             df = df.pivot_table(
                 index=DTTM_ALIAS,
                 columns=fd.get('groupby'),
                 values=self.metric_labels,
                 fill_value=0,
-                aggfunc=sum)
+                aggfunc=sum,
+                dropna=False,
+            )
 
         fm = fd.get('resample_fillmethod')
         if not fm:
@@ -1176,8 +1160,6 @@ def process_data(self, df, aggregate=False):
         rule = fd.get('resample_rule')
         if how and rule:
             df = df.resample(rule, how=how, fill_method=fm)
-            if not fm:
-                df = df.fillna(0)
 
         if self.sort_series:
             dfs = df.sum()
@@ -1241,7 +1223,6 @@ def get_data(self, df):
         fd = self.form_data
         comparison_type = fd.get('comparison_type') or 'values'
         df = self.process_data(df)
-
         if comparison_type == 'values':
             chart_data = self.to_series(df)
             for i, (label, df2) in enumerate(self._extra_chart_data):
@@ -1368,7 +1349,6 @@ def to_series(self, df, classed=''):
 
     def get_data(self, df):
         fd = self.form_data
-        df = df.fillna(0)
 
         if self.form_data.get('granularity') == 'all':
             raise Exception(_('Pick a time granularity for your time series'))
@@ -1377,7 +1357,9 @@ def get_data(self, df):
         metric_2 = utils.get_metric_name(fd.get('metric_2'))
         df = df.pivot_table(
             index=DTTM_ALIAS,
-            values=[metric, metric_2])
+            values=[metric, metric_2],
+            dropna=False,
+        )
 
         chart_data = self.to_series(df)
         return chart_data
@@ -1425,7 +1407,9 @@ def get_data(self, df):
         df = df.pivot_table(
             index=DTTM_ALIAS,
             columns='series',
-            values=utils.get_metric_name(fd.get('metric')))
+            values=utils.get_metric_name(fd.get('metric')),
+            dropna=False,
+        )
         chart_data = self.to_series(df)
         for serie in chart_data:
             serie['rank'] = rank_lookup[serie['key']]
@@ -1462,7 +1446,9 @@ def get_data(self, df):
         metric = self.metric_labels[0]
         df = df.pivot_table(
             index=self.groupby,
-            values=[metric])
+            values=[metric],
+            dropna=False,
+        )
         df.sort_values(by=metric, ascending=False, inplace=True)
         df = df.reset_index()
         df.columns = ['x', 'y']
@@ -1549,9 +1535,10 @@ def get_data(self, df):
         pt = df.pivot_table(
             index=self.groupby,
             columns=columns,
-            values=metrics)
+            values=metrics,
+            dropna=False,
+        )
         if fd.get('contribution'):
-            pt = pt.fillna(0)
             pt = pt.T
             pt = (pt / pt.sum()).T
         pt = pt.reindex(row.index)
@@ -2117,9 +2104,6 @@ class BaseDeckGLViz(BaseViz):
     credits = '<a href="https://uber.github.io/deck.gl/">deck.gl</a>'
     spatial_control_keys = []
 
-    def handle_nulls(self, df):
-        return df
-
     def get_metrics(self):
         self.metric = self.form_data.get('size')
         return [self.metric] if self.metric else []
@@ -2572,11 +2556,11 @@ def get_data(self, df):
         fd = self.form_data
         groups = fd.get('groupby')
         metrics = fd.get('metrics')
-        df.fillna(0)
         df = df.pivot_table(
             index=DTTM_ALIAS,
             columns=groups,
-            values=metrics)
+            values=metrics,
+        )
         cols = []
         # Be rid of falsey keys
         for col in df.columns:
@@ -2699,7 +2683,7 @@ def levels_for_time(self, groups, df):
         for i in range(0, len(groups) + 1):
             self.form_data['groupby'] = groups[:i]
             df_drop = df.drop(groups[i:], 1)
-            procs[i] = self.process_data(df_drop, aggregate=True).fillna(0)
+            procs[i] = self.process_data(df_drop, aggregate=True)
         self.form_data['groupby'] = groups
         return procs
 

tests/core_tests.py

@@ -622,16 +622,6 @@ def test_slice_query_endpoint(self):
         assert 'language' in resp
         self.logout()
 
-    def test_viz_get_fillna_for_columns(self):
-        slc = self.get_slice('Girls', db.session)
-        q = slc.viz.query_obj()
-        results = slc.viz.datasource.query(q)
-        fillna_columns = slc.viz.get_fillna_for_columns(results.df.columns)
-        self.assertDictEqual(
-            fillna_columns,
-            {'name': ' NULL', 'sum__num': 0},
-        )
-
     def test_import_csv(self):
         self.login(username='admin')
         filename = 'testCSV.csv'

tests/viz_tests.py

@@ -84,18 +84,6 @@ def test_process_metrics(self):
         self.assertEqual(test_viz.metric_labels, expect_metric_labels)
         self.assertEqual(test_viz.all_metrics, expect_metric_labels)
 
-    def test_get_fillna_returns_default_on_null_columns(self):
-        form_data = {
-            'viz_type': 'table',
-            'token': '12345',
-        }
-        datasource = self.get_datasource_mock()
-        test_viz = viz.BaseViz(datasource, form_data)
-        self.assertEqual(
-            test_viz.default_fillna,
-            test_viz.get_fillna_for_columns(),
-        )
-
     def test_get_df_returns_empty_df(self):
         form_data = {'dummy': 123}
         query_obj = {'granularity': 'day'}