[fix] Fix table viz column order (#9122)
This commit is contained in:
John Bodley
GitHub
parent
a7e433a512
commit
9f7466ef90
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@ -30,7 +30,6 @@ import re
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import uuid
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from collections import defaultdict, OrderedDict
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from datetime import datetime, timedelta
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from functools import reduce
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from itertools import product
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from typing import Any, Dict, List, Optional, Set, Tuple, TYPE_CHECKING
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@ -532,11 +531,13 @@ class TableViz(BaseViz):
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d = super().query_obj()
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fd = self.form_data
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if fd.get("all_columns") and (fd.get("groupby") or fd.get("metrics")):
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if fd.get("all_columns") and (
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fd.get("groupby") or fd.get("metrics") or fd.get("percent_metrics")
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):
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raise Exception(
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_(
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"Choose either fields to [Group By] and [Metrics] or "
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"[Columns], not both"
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"Choose either fields to [Group By] and [Metrics] and/or "
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"[Percentage Metrics], or [Columns], not both"
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)
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)
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@ -554,46 +555,50 @@ class TableViz(BaseViz):
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# Add all percent metrics that are not already in the list
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if "percent_metrics" in fd:
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d["metrics"] = d["metrics"] + list(
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filter(lambda m: m not in d["metrics"], fd["percent_metrics"] or [])
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d["metrics"].extend(
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m for m in fd["percent_metrics"] or [] if m not in d["metrics"]
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)
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d["is_timeseries"] = self.should_be_timeseries()
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return d
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def get_data(self, df: pd.DataFrame) -> VizData:
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fd = self.form_data
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if not self.should_be_timeseries() and df is not None and DTTM_ALIAS in df:
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"""
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Transform the query result to the table representation.
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:param df: The interim dataframe
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:returns: The table visualization data
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The interim dataframe comprises of the group-by and non-group-by columns and
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the union of the metrics representing the non-percent and percent metrics. Note
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the percent metrics have yet to be transformed.
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"""
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if not self.should_be_timeseries() and DTTM_ALIAS in df:
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del df[DTTM_ALIAS]
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# Sum up and compute percentages for all percent metrics
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percent_metrics = fd.get("percent_metrics") or []
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percent_metrics = [utils.get_metric_name(m) for m in percent_metrics]
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# Transform the data frame to adhere to the UI ordering of the columns and
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# metrics whilst simultaneously computing the percentages (via normalization)
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# for the percent metrics.
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non_percent_metric_columns = (
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self.form_data.get("all_columns") or self.form_data.get("groupby") or []
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) + utils.get_metric_names(self.form_data.get("metrics") or [])
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if len(percent_metrics):
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percent_metrics = list(filter(lambda m: m in df, percent_metrics))
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metric_sums = {
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m: reduce(lambda a, b: a + b, df[m]) for m in percent_metrics
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}
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metric_percents = {
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m: list(
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map(
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lambda a: None if metric_sums[m] == 0 else a / metric_sums[m],
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df[m],
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)
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)
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for m in percent_metrics
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}
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for m in percent_metrics:
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m_name = "%" + m
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df[m_name] = pd.Series(metric_percents[m], name=m_name)
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# Remove metrics that are not in the main metrics list
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metrics = fd.get("metrics") or []
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metrics = [utils.get_metric_name(m) for m in metrics]
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for m in filter(
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lambda m: m not in metrics and m in df.columns, percent_metrics
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):
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del df[m]
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percent_metric_columns = utils.get_metric_names(
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self.form_data.get("percent_metrics") or []
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)
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df = pd.concat(
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[
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df[non_percent_metric_columns],
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(
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df[percent_metric_columns]
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.div(df[percent_metric_columns].sum())
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.add_prefix("%")
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),
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],
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axis=1,
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)
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data = self.handle_js_int_overflow(
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dict(records=df.to_dict(orient="records"), columns=list(df.columns))
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@ -90,12 +90,12 @@ GB_RESULT_SET = [
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{
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"version": "v1",
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"timestamp": "2012-01-01T00:00:00.000Z",
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"event": {"dim1": "Canada", "dim2": "boy", "metric1": 12345678},
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"event": {"dim1": "Canada", "dim2": "boy", "count": 12345678},
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},
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{
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"version": "v1",
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"timestamp": "2012-01-01T00:00:00.000Z",
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"event": {"dim1": "USA", "dim2": "girl", "metric1": 12345678 / 2},
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"event": {"dim1": "USA", "dim2": "girl", "count": 12345678 / 2},
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},
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]
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@ -169,15 +169,7 @@ class BaseVizTestCase(SupersetTestCase):
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class TableVizTestCase(SupersetTestCase):
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def test_get_data_applies_percentage(self):
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form_data = {
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"percent_metrics": [
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{
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"expressionType": "SIMPLE",
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"aggregate": "SUM",
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"label": "SUM(value1)",
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"column": {"column_name": "value1", "type": "DOUBLE"},
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},
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"avg__B",
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],
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"groupby": ["groupA", "groupB"],
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"metrics": [
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{
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"expressionType": "SIMPLE",
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@ -188,39 +180,50 @@ class TableVizTestCase(SupersetTestCase):
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"count",
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"avg__C",
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],
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"percent_metrics": [
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{
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"expressionType": "SIMPLE",
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"aggregate": "SUM",
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"label": "SUM(value1)",
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"column": {"column_name": "value1", "type": "DOUBLE"},
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},
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"avg__B",
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],
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}
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datasource = self.get_datasource_mock()
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raw = {}
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raw["SUM(value1)"] = [15, 20, 25, 40]
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raw["avg__B"] = [10, 20, 5, 15]
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raw["avg__C"] = [11, 22, 33, 44]
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raw["count"] = [6, 7, 8, 9]
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raw["groupA"] = ["A", "B", "C", "C"]
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raw["groupB"] = ["x", "x", "y", "z"]
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df = pd.DataFrame(raw)
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df = pd.DataFrame(
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{
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"SUM(value1)": [15, 20, 25, 40],
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"avg__B": [10, 20, 5, 15],
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"avg__C": [11, 22, 33, 44],
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"count": [6, 7, 8, 9],
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"groupA": ["A", "B", "C", "C"],
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"groupB": ["x", "x", "y", "z"],
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}
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)
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test_viz = viz.TableViz(datasource, form_data)
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data = test_viz.get_data(df)
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# Check method correctly transforms data and computes percents
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self.assertEqual(
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set(
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[
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"groupA",
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"groupB",
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"count",
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"SUM(value1)",
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"avg__C",
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"%SUM(value1)",
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"%avg__B",
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]
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),
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set(data["columns"]),
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[
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"groupA",
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"groupB",
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"SUM(value1)",
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"count",
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"avg__C",
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"%SUM(value1)",
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"%avg__B",
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],
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list(data["columns"]),
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)
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expected = [
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{
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"groupA": "A",
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"groupB": "x",
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"count": 6,
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"SUM(value1)": 15,
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"count": 6,
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"avg__C": 11,
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"%SUM(value1)": 0.15,
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"%avg__B": 0.2,
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@ -228,8 +231,8 @@ class TableVizTestCase(SupersetTestCase):
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{
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"groupA": "B",
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"groupB": "x",
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"count": 7,
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"SUM(value1)": 20,
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"count": 7,
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"avg__C": 22,
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"%SUM(value1)": 0.2,
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"%avg__B": 0.4,
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@ -237,8 +240,8 @@ class TableVizTestCase(SupersetTestCase):
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{
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"groupA": "C",
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"groupB": "y",
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"count": 8,
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"SUM(value1)": 25,
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"count": 8,
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"avg__C": 33,
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"%SUM(value1)": 0.25,
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"%avg__B": 0.1,
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@ -246,10 +249,10 @@ class TableVizTestCase(SupersetTestCase):
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{
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"groupA": "C",
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"groupB": "z",
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"count": 9,
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"SUM(value1)": 40,
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"count": 9,
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"avg__C": 44,
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"%SUM(value1)": 0.40,
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"%SUM(value1)": 0.4,
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"%avg__B": 0.3,
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},
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]
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