feat: send post-processed data in reports (#15953)

* feat: send post-processed data in reports

* Fix tests and lint

* Use enums

* Limit Slack message to 4k chars

superset/charts/api.py

@@ -52,7 +52,7 @@ from superset.charts.commands.importers.dispatcher import ImportChartsCommand
 from superset.charts.commands.update import UpdateChartCommand
 from superset.charts.dao import ChartDAO
 from superset.charts.filters import ChartAllTextFilter, ChartFavoriteFilter, ChartFilter
-from superset.charts.post_processing import post_processors
+from superset.charts.post_processing import apply_post_process
 from superset.charts.schemas import (
     CHART_SCHEMAS,
     ChartPostSchema,
@@ -482,10 +482,7 @@ class ChartRestApi(BaseSupersetModelRestApi):
             return self.response_422(message=str(ex))
 
     def send_chart_response(
-        self,
+        self, result: Dict[Any, Any], form_data: Optional[Dict[str, Any]] = None,
-        result: Dict[Any, Any],
-        viz_type: Optional[str] = None,
-        form_data: Optional[Dict[str, Any]] = None,
     ) -> Response:
         result_type = result["query_context"].result_type
         result_format = result["query_context"].result_format
@@ -495,10 +492,9 @@ class ChartRestApi(BaseSupersetModelRestApi):
         # post-processing of data, eg, the pivot table.
         if (
             result_type == ChartDataResultType.POST_PROCESSED
-            and viz_type in post_processors
+            and result_format == ChartDataResultFormat.CSV
         ):
-            post_process = post_processors[viz_type]
+            result = apply_post_process(result, form_data)
-            result = post_process(result, form_data)
 
         if result_format == ChartDataResultFormat.CSV:
             # Verify user has permission to export CSV file
@@ -525,7 +521,6 @@ class ChartRestApi(BaseSupersetModelRestApi):
         self,
         command: ChartDataCommand,
         force_cached: bool = False,
-        viz_type: Optional[str] = None,
         form_data: Optional[Dict[str, Any]] = None,
     ) -> Response:
         try:
@@ -535,7 +530,7 @@ class ChartRestApi(BaseSupersetModelRestApi):
         except ChartDataQueryFailedError as exc:
             return self.response_400(message=exc.message)
 
-        return self.send_chart_response(result, viz_type, form_data)
+        return self.send_chart_response(result, form_data)
 
     @expose("/<int:pk>/data/", methods=["GET"])
     @protect()
@@ -637,9 +632,7 @@ class ChartRestApi(BaseSupersetModelRestApi):
         except (TypeError, json.decoder.JSONDecodeError):
             form_data = {}
 
-        return self.get_data_response(
+        return self.get_data_response(command, form_data=form_data)
-            command, viz_type=chart.viz_type, form_data=form_data
-        )
 
     @expose("/data", methods=["POST"])
     @protect()

superset/charts/post_processing.py

@@ -26,6 +26,7 @@ In order to do that, we reproduce the post-processing in Python
 for these chart types.
 """
 
+from io import StringIO
 from typing import Any, Callable, Dict, Optional, Union
 
 import pandas as pd
@@ -40,66 +41,48 @@ def sql_like_sum(series: pd.Series) -> pd.Series:
     return series.sum(min_count=1)
 
 
-def pivot_table(
+def pivot_table(df: pd.DataFrame, form_data: Dict[str, Any]) -> pd.DataFrame:
-    result: Dict[Any, Any], form_data: Optional[Dict[str, Any]] = None
-) -> Dict[Any, Any]:
     """
     Pivot table.
     """
-    for query in result["queries"]:
+    if form_data.get("granularity") == "all" and DTTM_ALIAS in df:
-        data = query["data"]
+        del df[DTTM_ALIAS]
-        df = pd.DataFrame(data)
-        form_data = form_data or {}
 
-        if form_data.get("granularity") == "all" and DTTM_ALIAS in df:
+    metrics = [get_metric_name(m) for m in form_data["metrics"]]
-            del df[DTTM_ALIAS]
+    aggfuncs: Dict[str, Union[str, Callable[[Any], Any]]] = {}
+    for metric in metrics:
+        aggfunc = form_data.get("pandas_aggfunc") or "sum"
+        if pd.api.types.is_numeric_dtype(df[metric]):
+            if aggfunc == "sum":
+                aggfunc = sql_like_sum
+        elif aggfunc not in {"min", "max"}:
+            aggfunc = "max"
+        aggfuncs[metric] = aggfunc
 
-        metrics = [get_metric_name(m) for m in form_data["metrics"]]
+    groupby = form_data.get("groupby") or []
-        aggfuncs: Dict[str, Union[str, Callable[[Any], Any]]] = {}
+    columns = form_data.get("columns") or []
-        for metric in metrics:
+    if form_data.get("transpose_pivot"):
-            aggfunc = form_data.get("pandas_aggfunc") or "sum"
+        groupby, columns = columns, groupby
-            if pd.api.types.is_numeric_dtype(df[metric]):
-                if aggfunc == "sum":
-                    aggfunc = sql_like_sum
-            elif aggfunc not in {"min", "max"}:
-                aggfunc = "max"
-            aggfuncs[metric] = aggfunc
 
-        groupby = form_data.get("groupby") or []
+    df = df.pivot_table(
-        columns = form_data.get("columns") or []
+        index=groupby,
-        if form_data.get("transpose_pivot"):
+        columns=columns,
-            groupby, columns = columns, groupby
+        values=metrics,
+        aggfunc=aggfuncs,
+        margins=form_data.get("pivot_margins"),
+    )
 
-        df = df.pivot_table(
+    # Re-order the columns adhering to the metric ordering.
-            index=groupby,
+    df = df[metrics]
-            columns=columns,
-            values=metrics,
-            aggfunc=aggfuncs,
-            margins=form_data.get("pivot_margins"),
-        )
 
-        # Re-order the columns adhering to the metric ordering.
+    # Display metrics side by side with each column
-        df = df[metrics]
+    if form_data.get("combine_metric"):
+        df = df.stack(0).unstack().reindex(level=-1, columns=metrics)
 
-        # Display metrics side by side with each column
+    # flatten column names
-        if form_data.get("combine_metric"):
+    df.columns = [" ".join(column) for column in df.columns]
-            df = df.stack(0).unstack().reindex(level=-1, columns=metrics)
 
-        # flatten column names
+    return df
-        df.columns = [" ".join(column) for column in df.columns]
-
-        # re-arrange data into a list of dicts
-        data = []
-        for i in df.index:
-            row = {col: df[col][i] for col in df.columns}
-            row[df.index.name] = i
-            data.append(row)
-        query["data"] = data
-        query["colnames"] = list(df.columns)
-        query["coltypes"] = extract_dataframe_dtypes(df)
-        query["rowcount"] = len(df.index)
-
-    return result
 
 
 def list_unique_values(series: pd.Series) -> str:
@@ -134,88 +117,99 @@ pivot_v2_aggfunc_map = {
 
 
 def pivot_table_v2(  # pylint: disable=too-many-branches
-    result: Dict[Any, Any], form_data: Optional[Dict[str, Any]] = None,
+    df: pd.DataFrame, form_data: Dict[str, Any]
-) -> Dict[Any, Any]:
+) -> pd.DataFrame:
     """
     Pivot table v2.
     """
-    for query in result["queries"]:
+    if form_data.get("granularity_sqla") == "all" and DTTM_ALIAS in df:
-        data = query["data"]
+        del df[DTTM_ALIAS]
-        df = pd.DataFrame(data)
-        form_data = form_data or {}
 
-        if form_data.get("granularity_sqla") == "all" and DTTM_ALIAS in df:
+    # TODO (betodealmeida): implement metricsLayout
-            del df[DTTM_ALIAS]
+    metrics = [get_metric_name(m) for m in form_data["metrics"]]
+    aggregate_function = form_data.get("aggregateFunction", "Sum")
+    groupby = form_data.get("groupbyRows") or []
+    columns = form_data.get("groupbyColumns") or []
+    if form_data.get("transposePivot"):
+        groupby, columns = columns, groupby
 
-        # TODO (betodealmeida): implement metricsLayout
+    df = df.pivot_table(
-        metrics = [get_metric_name(m) for m in form_data["metrics"]]
+        index=groupby,
-        aggregate_function = form_data.get("aggregateFunction", "Sum")
+        columns=columns,
-        groupby = form_data.get("groupbyRows") or []
+        values=metrics,
-        columns = form_data.get("groupbyColumns") or []
+        aggfunc=pivot_v2_aggfunc_map[aggregate_function],
-        if form_data.get("transposePivot"):
+        margins=True,
-            groupby, columns = columns, groupby
+    )
 
-        df = df.pivot_table(
+    # The pandas `pivot_table` method either brings both row/column
-            index=groupby,
+    # totals, or none at all. We pass `margin=True` to get both, and
-            columns=columns,
+    # remove any dimension that was not requests.
-            values=metrics,
+    if not form_data.get("rowTotals"):
-            aggfunc=pivot_v2_aggfunc_map[aggregate_function],
+        df.drop(df.columns[len(df.columns) - 1], axis=1, inplace=True)
-            margins=True,
+    if not form_data.get("colTotals"):
-        )
+        df = df[:-1]
 
-        # The pandas `pivot_table` method either brings both row/column
+    # Compute fractions, if needed. If `colTotals` or `rowTotals` are
-        # totals, or none at all. We pass `margin=True` to get both, and
+    # present we need to adjust for including them in the sum
-        # remove any dimension that was not requests.
+    if aggregate_function.endswith(" as Fraction of Total"):
-        if not form_data.get("rowTotals"):
+        total = df.sum().sum()
-            df.drop(df.columns[len(df.columns) - 1], axis=1, inplace=True)
+        df = df.astype(total.dtypes) / total
-        if not form_data.get("colTotals"):
+        if form_data.get("colTotals"):
-            df = df[:-1]
+            df *= 2
+        if form_data.get("rowTotals"):
+            df *= 2
+    elif aggregate_function.endswith(" as Fraction of Columns"):
+        total = df.sum(axis=0)
+        df = df.astype(total.dtypes).div(total, axis=1)
+        if form_data.get("colTotals"):
+            df *= 2
+    elif aggregate_function.endswith(" as Fraction of Rows"):
+        total = df.sum(axis=1)
+        df = df.astype(total.dtypes).div(total, axis=0)
+        if form_data.get("rowTotals"):
+            df *= 2
 
-        # Compute fractions, if needed. If `colTotals` or `rowTotals` are
+    # Re-order the columns adhering to the metric ordering.
-        # present we need to adjust for including them in the sum
+    df = df[metrics]
-        if aggregate_function.endswith(" as Fraction of Total"):
-            total = df.sum().sum()
-            df = df.astype(total.dtypes) / total
-            if form_data.get("colTotals"):
-                df *= 2
-            if form_data.get("rowTotals"):
-                df *= 2
-        elif aggregate_function.endswith(" as Fraction of Columns"):
-            total = df.sum(axis=0)
-            df = df.astype(total.dtypes).div(total, axis=1)
-            if form_data.get("colTotals"):
-                df *= 2
-        elif aggregate_function.endswith(" as Fraction of Rows"):
-            total = df.sum(axis=1)
-            df = df.astype(total.dtypes).div(total, axis=0)
-            if form_data.get("rowTotals"):
-                df *= 2
 
-        # Re-order the columns adhering to the metric ordering.
+    # Display metrics side by side with each column
-        df = df[metrics]
+    if form_data.get("combineMetric"):
+        df = df.stack(0).unstack().reindex(level=-1, columns=metrics)
 
-        # Display metrics side by side with each column
+    # flatten column names
-        if form_data.get("combineMetric"):
+    df.columns = [" ".join(column) for column in df.columns]
-            df = df.stack(0).unstack().reindex(level=-1, columns=metrics)
 
-        # flatten column names
+    return df
-        df.columns = [" ".join(column) for column in df.columns]
-
-        # re-arrange data into a list of dicts
-        data = []
-        for i in df.index:
-            row = {col: df[col][i] for col in df.columns}
-            row[df.index.name] = i
-            data.append(row)
-        query["data"] = data
-        query["colnames"] = list(df.columns)
-        query["coltypes"] = extract_dataframe_dtypes(df)
-        query["rowcount"] = len(df.index)
-
-    return result
 
 
 post_processors = {
     "pivot_table": pivot_table,
     "pivot_table_v2": pivot_table_v2,
 }
+
+
+def apply_post_process(
+    result: Dict[Any, Any], form_data: Optional[Dict[str, Any]] = None,
+) -> Dict[Any, Any]:
+    form_data = form_data or {}
+
+    viz_type = form_data.get("viz_type")
+    if viz_type not in post_processors:
+        return result
+
+    post_processor = post_processors[viz_type]
+
+    for query in result["queries"]:
+        df = pd.read_csv(StringIO(query["data"]))
+        processed_df = post_processor(df, form_data)
+
+        buf = StringIO()
+        processed_df.to_csv(buf)
+        buf.seek(0)
+
+        query["data"] = buf.getvalue()
+        query["colnames"] = list(processed_df.columns)
+        query["coltypes"] = extract_dataframe_dtypes(processed_df)
+        query["rowcount"] = len(processed_df.index)
+
+    return result

superset/models/slice.py

@@ -55,7 +55,7 @@ logger = logging.getLogger(__name__)
 
 class Slice(
     Model, AuditMixinNullable, ImportExportMixin
-):  # pylint: disable=too-many-public-methods
+):  # pylint: disable=too-many-public-methods, too-many-instance-attributes
 
     """A slice is essentially a report or a view on data"""
 

superset/reports/commands/execute.py

@@ -64,6 +64,7 @@ from superset.reports.notifications import create_notification
 from superset.reports.notifications.base import NotificationContent
 from superset.reports.notifications.exceptions import NotificationError
 from superset.utils.celery import session_scope
+from superset.utils.core import ChartDataResultFormat, ChartDataResultType
 from superset.utils.csv import get_chart_csv_data
 from superset.utils.screenshots import (
     BaseScreenshot,
@@ -146,7 +147,8 @@ class BaseReportState:
                 return get_url_path(
                     "ChartRestApi.get_data",
                     pk=self._report_schedule.chart_id,
-                    format="csv",
+                    format=ChartDataResultFormat.CSV.value,
+                    type=ChartDataResultType.POST_PROCESSED.value,
                 )
             return get_url_path(
                 "Superset.slice",

superset/reports/notifications/slack.py

@@ -17,7 +17,6 @@
 # under the License.
 import json
 import logging
-import textwrap
 from io import IOBase
 from typing import Optional, Union
 
@@ -34,8 +33,8 @@ from superset.reports.notifications.exceptions import NotificationError
 
 logger = logging.getLogger(__name__)
 
-# Slack only shows ~25 lines in the code block section
+# Slack only allows Markdown messages up to 4k chars
-MAXIMUM_ROWS_IN_CODE_SECTION = 21
+MAXIMUM_MESSAGE_SIZE = 4000
 
 
 class SlackNotification(BaseNotification):  # pylint: disable=too-few-public-methods
@@ -48,43 +47,7 @@ class SlackNotification(BaseNotification):  # pylint: disable=too-few-public-met
     def _get_channel(self) -> str:
         return json.loads(self._recipient.recipient_config_json)["target"]
 
-    @staticmethod
+    def _message_template(self, table: str = "") -> str:
-    def _error_template(name: str, description: str, text: str) -> str:
-        return textwrap.dedent(
-            __(
-                """
-            *%(name)s*\n
-            %(description)s\n
-            Error: %(text)s
-            """,
-                name=name,
-                description=description,
-                text=text,
-            )
-        )
-
-    def _get_body(self) -> str:
-        if self._content.text:
-            return self._error_template(
-                self._content.name, self._content.description or "", self._content.text
-            )
-
-        # Convert Pandas dataframe into a nice ASCII table
-        if self._content.embedded_data is not None:
-            df = self._content.embedded_data
-
-            truncated = len(df) > MAXIMUM_ROWS_IN_CODE_SECTION
-            message = "(table was truncated)" if truncated else ""
-            if truncated:
-                df = df[:MAXIMUM_ROWS_IN_CODE_SECTION].fillna("")
-                # add a last row with '...' for values
-                df = df.append({k: "..." for k in df.columns}, ignore_index=True)
-
-            tabulated = tabulate(df, headers="keys", showindex=False)
-            table = f"```\n{tabulated}\n```\n\n{message}"
-        else:
-            table = ""
-
         return __(
             """*%(name)s*
 
@@ -93,13 +56,70 @@ class SlackNotification(BaseNotification):  # pylint: disable=too-few-public-met
 <%(url)s|Explore in Superset>
 
 %(table)s
-            """,
+""",
             name=self._content.name,
             description=self._content.description or "",
             url=self._content.url,
             table=table,
         )
 
+    @staticmethod
+    def _error_template(name: str, description: str, text: str) -> str:
+        return __(
+            """*%(name)s*
+
+%(description)s
+
+Error: %(text)s
+""",
+            name=name,
+            description=description,
+            text=text,
+        )
+
+    def _get_body(self) -> str:
+        if self._content.text:
+            return self._error_template(
+                self._content.name, self._content.description or "", self._content.text
+            )
+
+        if self._content.embedded_data is None:
+            return self._message_template()
+
+        # Embed data in the message
+        df = self._content.embedded_data
+
+        # Slack Markdown only works on messages shorter than 4k chars, so we might
+        # need to truncate the data
+        for i in range(len(df) - 1):
+            truncated_df = df[: i + 1].fillna("")
+            truncated_df = truncated_df.append(
+                {k: "..." for k in df.columns}, ignore_index=True
+            )
+            tabulated = tabulate(truncated_df, headers="keys", showindex=False)
+            table = f"```\n{tabulated}\n```\n\n(table was truncated)"
+            message = self._message_template(table)
+            if len(message) > MAXIMUM_MESSAGE_SIZE:
+                # Decrement i and build a message that is under the limit
+                truncated_df = df[:i].fillna("")
+                truncated_df = truncated_df.append(
+                    {k: "..." for k in df.columns}, ignore_index=True
+                )
+                tabulated = tabulate(truncated_df, headers="keys", showindex=False)
+                table = (
+                    f"```\n{tabulated}\n```\n\n(table was truncated)"
+                    if len(truncated_df) > 0
+                    else ""
+                )
+                break
+
+        # Send full data
+        else:
+            tabulated = tabulate(df, headers="keys", showindex=False)
+            table = f"```\n{tabulated}\n```"
+
+        return self._message_template(table)
+
     def _get_inline_file(self) -> Optional[Union[str, IOBase, bytes]]:
         if self._content.csv:
             return self._content.csv

tests/unit_tests/charts/test_post_processing.py

@@ -18,7 +18,7 @@
 import copy
 from typing import Any, Dict
 
-from superset.charts.post_processing import pivot_table, pivot_table_v2
+from superset.charts.post_processing import apply_post_process
 from superset.utils.core import GenericDataType, QueryStatus
 
 RESULT: Dict[str, Any] = {
@@ -51,30 +51,30 @@ LIMIT 50000;
                 GenericDataType.STRING,
                 GenericDataType.NUMERIC,
             ],
-            "data": [
+            "data": """state,gender,Births
-                {"state": "OH", "gender": "boy", "Births": int("2376385")},
+OH,boy,2376385
-                {"state": "TX", "gender": "girl", "Births": int("2313186")},
+TX,girl,2313186
-                {"state": "MA", "gender": "boy", "Births": int("1285126")},
+MA,boy,1285126
-                {"state": "MA", "gender": "girl", "Births": int("842146")},
+MA,girl,842146
-                {"state": "PA", "gender": "boy", "Births": int("2390275")},
+PA,boy,2390275
-                {"state": "NY", "gender": "boy", "Births": int("3543961")},
+NY,boy,3543961
-                {"state": "FL", "gender": "boy", "Births": int("1968060")},
+FL,boy,1968060
-                {"state": "TX", "gender": "boy", "Births": int("3311985")},
+TX,boy,3311985
-                {"state": "NJ", "gender": "boy", "Births": int("1486126")},
+NJ,boy,1486126
-                {"state": "CA", "gender": "girl", "Births": int("3567754")},
+CA,girl,3567754
-                {"state": "CA", "gender": "boy", "Births": int("5430796")},
+CA,boy,5430796
-                {"state": "IL", "gender": "girl", "Births": int("1614427")},
+IL,girl,1614427
-                {"state": "FL", "gender": "girl", "Births": int("1312593")},
+FL,girl,1312593
-                {"state": "NY", "gender": "girl", "Births": int("2280733")},
+NY,girl,2280733
-                {"state": "NJ", "gender": "girl", "Births": int("992702")},
+NJ,girl,992702
-                {"state": "MI", "gender": "girl", "Births": int("1326229")},
+MI,girl,1326229
-                {"state": "other", "gender": "girl", "Births": int("15058341")},
+other,girl,15058341
-                {"state": "other", "gender": "boy", "Births": int("22044909")},
+other,boy,22044909
-                {"state": "MI", "gender": "boy", "Births": int("1938321")},
+MI,boy,1938321
-                {"state": "IL", "gender": "boy", "Births": int("2357411")},
+IL,boy,2357411
-                {"state": "PA", "gender": "girl", "Births": int("1615383")},
+PA,girl,1615383
-                {"state": "OH", "gender": "girl", "Births": int("1622814")},
+OH,girl,1622814
-            ],
+            """,
             "applied_filters": [],
             "rejected_filters": [],
         }
@@ -113,7 +113,7 @@ def test_pivot_table():
         "viz_type": "pivot_table",
     }
     result = copy.deepcopy(RESULT)
-    assert pivot_table(result, form_data) == {
+    assert apply_post_process(result, form_data) == {
         "query_context": None,
         "queries": [
             {
@@ -165,53 +165,11 @@ LIMIT 50000;
                     GenericDataType.NUMERIC,
                     GenericDataType.NUMERIC,
                 ],
-                "data": [
+                "data": """gender,Births CA,Births FL,Births IL,Births MA,Births MI,Births NJ,Births NY,Births OH,Births PA,Births TX,Births other,Births All
-                    {
+boy,5430796,1968060,2357411,1285126,1938321,1486126,3543961,2376385,2390275,3311985,22044909,48133355
-                        "Births CA": 5430796,
+girl,3567754,1312593,1614427,842146,1326229,992702,2280733,1622814,1615383,2313186,15058341,32546308
-                        "Births FL": 1968060,
+All,8998550,3280653,3971838,2127272,3264550,2478828,5824694,3999199,4005658,5625171,37103250,80679663
-                        "Births IL": 2357411,
+""",
-                        "Births MA": 1285126,
-                        "Births MI": 1938321,
-                        "Births NJ": 1486126,
-                        "Births NY": 3543961,
-                        "Births OH": 2376385,
-                        "Births PA": 2390275,
-                        "Births TX": 3311985,
-                        "Births other": 22044909,
-                        "Births All": 48133355,
-                        "gender": "boy",
-                    },
-                    {
-                        "Births CA": 3567754,
-                        "Births FL": 1312593,
-                        "Births IL": 1614427,
-                        "Births MA": 842146,
-                        "Births MI": 1326229,
-                        "Births NJ": 992702,
-                        "Births NY": 2280733,
-                        "Births OH": 1622814,
-                        "Births PA": 1615383,
-                        "Births TX": 2313186,
-                        "Births other": 15058341,
-                        "Births All": 32546308,
-                        "gender": "girl",
-                    },
-                    {
-                        "Births CA": 8998550,
-                        "Births FL": 3280653,
-                        "Births IL": 3971838,
-                        "Births MA": 2127272,
-                        "Births MI": 3264550,
-                        "Births NJ": 2478828,
-                        "Births NY": 5824694,
-                        "Births OH": 3999199,
-                        "Births PA": 4005658,
-                        "Births TX": 5625171,
-                        "Births other": 37103250,
-                        "Births All": 80679663,
-                        "gender": "All",
-                    },
-                ],
                 "applied_filters": [],
                 "rejected_filters": [],
             }
@@ -255,7 +213,7 @@ def test_pivot_table_v2():
         "viz_type": "pivot_table_v2",
     }
     result = copy.deepcopy(RESULT)
-    assert pivot_table_v2(result, form_data) == {
+    assert apply_post_process(result, form_data) == {
         "query_context": None,
         "queries": [
             {
@@ -285,80 +243,20 @@ LIMIT 50000;
                     GenericDataType.NUMERIC,
                     GenericDataType.NUMERIC,
                 ],
-                "data": [
+                "data": """state,All Births,boy Births,girl Births
-                    {
+All,1.0,0.5965983645717509,0.40340163542824914
-                        "All Births": 1.0,
+CA,1.0,0.6035190113962805,0.3964809886037195
-                        "boy Births": 0.5965983645717509,
+FL,1.0,0.5998988615985903,0.4001011384014097
-                        "girl Births": 0.40340163542824914,
+IL,1.0,0.5935315085862012,0.40646849141379887
-                        "state": "All",
+MA,1.0,0.6041192663655611,0.3958807336344389
-                    },
+MI,1.0,0.5937482960898133,0.4062517039101867
-                    {
+NJ,1.0,0.5995276800165239,0.40047231998347604
-                        "All Births": 1.0,
+NY,1.0,0.6084372844307357,0.39156271556926425
-                        "boy Births": 0.6035190113962805,
+OH,1.0,0.5942152416021308,0.40578475839786915
-                        "girl Births": 0.3964809886037195,
+PA,1.0,0.596724682935987,0.40327531706401293
-                        "state": "CA",
+TX,1.0,0.5887794344385264,0.41122056556147357
-                    },
+other,1.0,0.5941503507105172,0.40584964928948275
-                    {
+""",
-                        "All Births": 1.0,
-                        "boy Births": 0.5998988615985903,
-                        "girl Births": 0.4001011384014097,
-                        "state": "FL",
-                    },
-                    {
-                        "All Births": 1.0,
-                        "boy Births": 0.5935315085862012,
-                        "girl Births": 0.40646849141379887,
-                        "state": "IL",
-                    },
-                    {
-                        "All Births": 1.0,
-                        "boy Births": 0.6041192663655611,
-                        "girl Births": 0.3958807336344389,
-                        "state": "MA",
-                    },
-                    {
-                        "All Births": 1.0,
-                        "boy Births": 0.5937482960898133,
-                        "girl Births": 0.4062517039101867,
-                        "state": "MI",
-                    },
-                    {
-                        "All Births": 1.0,
-                        "boy Births": 0.5995276800165239,
-                        "girl Births": 0.40047231998347604,
-                        "state": "NJ",
-                    },
-                    {
-                        "All Births": 1.0,
-                        "boy Births": 0.6084372844307357,
-                        "girl Births": 0.39156271556926425,
-                        "state": "NY",
-                    },
-                    {
-                        "All Births": 1.0,
-                        "boy Births": 0.5942152416021308,
-                        "girl Births": 0.40578475839786915,
-                        "state": "OH",
-                    },
-                    {
-                        "All Births": 1.0,
-                        "boy Births": 0.596724682935987,
-                        "girl Births": 0.40327531706401293,
-                        "state": "PA",
-                    },
-                    {
-                        "All Births": 1.0,
-                        "boy Births": 0.5887794344385264,
-                        "girl Births": 0.41122056556147357,
-                        "state": "TX",
-                    },
-                    {
-                        "All Births": 1.0,
-                        "boy Births": 0.5941503507105172,
-                        "girl Births": 0.40584964928948275,
-                        "state": "other",
-                    },
-                ],
                 "applied_filters": [],
                 "rejected_filters": [],
             }