mirror of https://github.com/apache/superset.git
2033 lines
73 KiB
Python
2033 lines
73 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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import pandas as pd
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import pytest
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from flask_babel import lazy_gettext as _
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from numpy import True_
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from sqlalchemy.orm.session import Session
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from superset.charts.post_processing import apply_post_process, pivot_df, table
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from superset.common.chart_data import ChartDataResultFormat
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from superset.utils.core import GenericDataType
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def test_pivot_df_no_cols_no_rows_single_metric():
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"""
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Pivot table when no cols/rows and 1 metric are selected.
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"""
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# when no cols/rows are selected there are no groupbys in the query,
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# and the data has only the metric(s)
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df = pd.DataFrame.from_dict({"SUM(num)": {0: 80679663}})
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assert (
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df.to_markdown()
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== """
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| | SUM(num) |
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|---:|------------:|
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| 0 | 8.06797e+07 |
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""".strip()
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)
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pivoted = pivot_df(
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df,
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rows=[],
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columns=[],
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metrics=["SUM(num)"],
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aggfunc="Sum",
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transpose_pivot=False,
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combine_metrics=False,
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show_rows_total=False,
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show_columns_total=False,
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apply_metrics_on_rows=False,
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)
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assert (
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pivoted.to_markdown()
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== f"""
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| | ('SUM(num)',) |
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|:-----------------|----------------:|
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| ('{_("Total")} (Sum)',) | 8.06797e+07 |
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""".strip()
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)
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# transpose_pivot and combine_metrics do nothing in this case
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pivoted = pivot_df(
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df,
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rows=[],
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columns=[],
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metrics=["SUM(num)"],
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aggfunc="Sum",
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transpose_pivot=True,
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combine_metrics=True,
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show_rows_total=False,
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show_columns_total=False,
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apply_metrics_on_rows=False,
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)
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assert (
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pivoted.to_markdown()
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== f"""
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| | ('SUM(num)',) |
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|:-----------------|----------------:|
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| ('{_("Total")} (Sum)',) | 8.06797e+07 |
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""".strip()
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)
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# apply_metrics_on_rows will pivot the table, moving the metrics
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# to rows
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pivoted = pivot_df(
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df,
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rows=[],
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columns=[],
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metrics=["SUM(num)"],
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aggfunc="Sum",
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transpose_pivot=True,
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combine_metrics=True,
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show_rows_total=False,
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show_columns_total=False,
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apply_metrics_on_rows=True,
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)
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assert (
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pivoted.to_markdown()
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== f"""
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| | ('{_("Total")} (Sum)',) |
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|:--------------|-------------------:|
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| ('SUM(num)',) | 8.06797e+07 |
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""".strip()
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)
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# showing totals
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pivoted = pivot_df(
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df,
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rows=[],
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columns=[],
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metrics=["SUM(num)"],
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aggfunc="Sum",
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transpose_pivot=True,
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combine_metrics=True,
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show_rows_total=True,
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show_columns_total=True,
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apply_metrics_on_rows=False,
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)
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assert (
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pivoted.to_markdown()
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== f"""
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| | ('SUM(num)',) | ('Total (Sum)',) |
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|:-----------------|----------------:|-------------------:|
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| ('{_("Total")} (Sum)',) | 8.06797e+07 | 8.06797e+07 |
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""".strip()
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)
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def test_pivot_df_no_cols_no_rows_two_metrics():
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"""
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Pivot table when no cols/rows and 2 metrics are selected.
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"""
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# when no cols/rows are selected there are no groupbys in the query,
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# and the data has only the metrics
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df = pd.DataFrame.from_dict({"SUM(num)": {0: 80679663}, "MAX(num)": {0: 37296}})
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assert (
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df.to_markdown()
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== """
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| | SUM(num) | MAX(num) |
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|---:|------------:|-----------:|
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| 0 | 8.06797e+07 | 37296 |
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""".strip()
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)
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pivoted = pivot_df(
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df,
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rows=[],
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columns=[],
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metrics=["SUM(num)", "MAX(num)"],
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aggfunc="Sum",
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transpose_pivot=False,
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combine_metrics=False,
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show_rows_total=False,
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show_columns_total=False,
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apply_metrics_on_rows=False,
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)
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assert (
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pivoted.to_markdown()
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== f"""
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| | ('SUM(num)',) | ('MAX(num)',) |
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|:-----------------|----------------:|----------------:|
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| ('{_("Total")} (Sum)',) | 8.06797e+07 | 37296 |
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""".strip()
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)
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# transpose_pivot and combine_metrics do nothing in this case
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pivoted = pivot_df(
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df,
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rows=[],
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columns=[],
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metrics=["SUM(num)", "MAX(num)"],
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aggfunc="Sum",
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transpose_pivot=True,
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combine_metrics=True,
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show_rows_total=False,
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show_columns_total=False,
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apply_metrics_on_rows=False,
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)
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assert (
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pivoted.to_markdown()
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== """
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| | ('SUM(num)',) | ('MAX(num)',) |
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|:-----------------|----------------:|----------------:|
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| ('Total (Sum)',) | 8.06797e+07 | 37296 |
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""".strip()
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)
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# apply_metrics_on_rows will pivot the table, moving the metrics
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# to rows
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pivoted = pivot_df(
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df,
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rows=[],
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columns=[],
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metrics=["SUM(num)", "MAX(num)"],
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aggfunc="Sum",
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transpose_pivot=True,
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combine_metrics=True,
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show_rows_total=False,
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show_columns_total=False,
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apply_metrics_on_rows=True,
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)
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assert (
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pivoted.to_markdown()
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== f"""
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| | ('{_("Total")} (Sum)',) |
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|:--------------|-------------------:|
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| ('SUM(num)',) | 8.06797e+07 |
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| ('MAX(num)',) | 37296 |
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""".strip()
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)
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# when showing totals we only add a column, since adding a row
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# would be redundant
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pivoted = pivot_df(
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df,
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rows=[],
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columns=[],
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metrics=["SUM(num)", "MAX(num)"],
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aggfunc="Sum",
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transpose_pivot=True,
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combine_metrics=True,
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show_rows_total=True,
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show_columns_total=True,
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apply_metrics_on_rows=False,
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)
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assert (
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pivoted.to_markdown()
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== f"""
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| | ('SUM(num)',) | ('MAX(num)',) | ('{_("Total")} (Sum)',) |
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|:-----------------|----------------:|----------------:|-------------------:|
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| ('{_("Total")} (Sum)',) | 8.06797e+07 | 37296 | 8.0717e+07 |
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""".strip()
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)
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def test_pivot_df_single_row_two_metrics():
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"""
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Pivot table when a single column and 2 metrics are selected.
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"""
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df = pd.DataFrame.from_dict(
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{
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"gender": {0: "girl", 1: "boy"},
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"SUM(num)": {0: 118065, 1: 47123},
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"MAX(num)": {0: 2588, 1: 1280},
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}
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)
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assert (
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df.to_markdown()
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== """
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| | gender | SUM(num) | MAX(num) |
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|---:|:---------|-----------:|-----------:|
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| 0 | girl | 118065 | 2588 |
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| 1 | boy | 47123 | 1280 |
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""".strip()
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)
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pivoted = pivot_df(
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df,
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rows=["gender"],
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columns=[],
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metrics=["SUM(num)", "MAX(num)"],
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aggfunc="Sum",
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transpose_pivot=False,
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combine_metrics=False,
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show_rows_total=False,
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show_columns_total=False,
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apply_metrics_on_rows=False,
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)
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assert (
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pivoted.to_markdown()
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== """
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| | ('SUM(num)',) | ('MAX(num)',) |
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|:----------|----------------:|----------------:|
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| ('boy',) | 47123 | 1280 |
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| ('girl',) | 118065 | 2588 |
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""".strip()
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)
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# transpose_pivot
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pivoted = pivot_df(
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df,
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rows=["gender"],
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columns=[],
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metrics=["SUM(num)", "MAX(num)"],
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aggfunc="Sum",
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transpose_pivot=True,
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combine_metrics=False,
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show_rows_total=False,
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show_columns_total=False,
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apply_metrics_on_rows=False,
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)
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assert (
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pivoted.to_markdown()
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== f"""
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| | ('SUM(num)', 'boy') | ('SUM(num)', 'girl') | ('MAX(num)', 'boy') | ('MAX(num)', 'girl') |
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|:-----------------|----------------------:|-----------------------:|----------------------:|-----------------------:|
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| ('{_("Total")} (Sum)',) | 47123 | 118065 | 1280 | 2588 |
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""".strip()
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)
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# combine_metrics does nothing in this case
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pivoted = pivot_df(
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df,
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rows=["gender"],
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columns=[],
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metrics=["SUM(num)", "MAX(num)"],
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aggfunc="Sum",
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transpose_pivot=False,
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combine_metrics=True,
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show_rows_total=False,
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show_columns_total=False,
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apply_metrics_on_rows=False,
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)
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assert (
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pivoted.to_markdown()
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== """
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| | ('SUM(num)',) | ('MAX(num)',) |
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|:----------|----------------:|----------------:|
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| ('boy',) | 47123 | 1280 |
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| ('girl',) | 118065 | 2588 |
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""".strip()
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)
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# show totals
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pivoted = pivot_df(
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df,
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rows=["gender"],
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columns=[],
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metrics=["SUM(num)", "MAX(num)"],
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aggfunc="Sum",
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transpose_pivot=False,
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combine_metrics=False,
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show_rows_total=True,
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show_columns_total=True,
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apply_metrics_on_rows=False,
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)
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assert (
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pivoted.to_markdown()
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== f"""
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| | ('SUM(num)',) | ('MAX(num)',) | ('{_("Total")} (Sum)',) |
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|:-----------------|----------------:|----------------:|-------------------:|
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| ('boy',) | 47123 | 1280 | 48403 |
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| ('girl',) | 118065 | 2588 | 120653 |
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| ('{_("Total")} (Sum)',) | 165188 | 3868 | 169056 |
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""".strip()
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)
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# apply_metrics_on_rows
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pivoted = pivot_df(
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df,
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rows=["gender"],
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columns=[],
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metrics=["SUM(num)", "MAX(num)"],
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aggfunc="Sum",
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transpose_pivot=False,
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combine_metrics=False,
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show_rows_total=True,
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show_columns_total=True,
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apply_metrics_on_rows=True,
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)
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assert (
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pivoted.to_markdown()
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== f"""
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| | ('{_("Total")} (Sum)',) |
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|:-------------------------|-------------------:|
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| ('SUM(num)', 'boy') | 47123 |
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| ('SUM(num)', 'girl') | 118065 |
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| ('SUM(num)', 'Subtotal') | 165188 |
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| ('MAX(num)', 'boy') | 1280 |
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| ('MAX(num)', 'girl') | 2588 |
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| ('MAX(num)', 'Subtotal') | 3868 |
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| ('{_("Total")} (Sum)', '') | 169056 |
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""".strip()
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)
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# apply_metrics_on_rows with combine_metrics
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pivoted = pivot_df(
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df,
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rows=["gender"],
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columns=[],
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metrics=["SUM(num)", "MAX(num)"],
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aggfunc="Sum",
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transpose_pivot=False,
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combine_metrics=True,
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show_rows_total=True,
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show_columns_total=True,
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apply_metrics_on_rows=True,
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)
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assert (
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pivoted.to_markdown()
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== f"""
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| | ('{_("Total")} (Sum)',) |
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|:---------------------|-------------------:|
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| ('boy', 'SUM(num)') | 47123 |
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| ('boy', 'MAX(num)') | 1280 |
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| ('boy', 'Subtotal') | 48403 |
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| ('girl', 'SUM(num)') | 118065 |
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| ('girl', 'MAX(num)') | 2588 |
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| ('girl', 'Subtotal') | 120653 |
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| ('{_("Total")} (Sum)', '') | 169056 |
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""".strip()
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)
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def test_pivot_df_complex():
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"""
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Pivot table when a column, rows and 2 metrics are selected.
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"""
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df = pd.DataFrame.from_dict(
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{
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"state": {
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0: "CA",
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1: "CA",
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2: "CA",
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3: "FL",
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4: "CA",
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5: "CA",
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6: "FL",
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7: "FL",
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8: "FL",
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9: "CA",
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10: "FL",
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11: "FL",
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},
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"gender": {
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0: "girl",
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1: "boy",
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2: "girl",
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3: "girl",
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4: "girl",
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5: "girl",
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6: "boy",
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7: "girl",
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8: "girl",
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9: "boy",
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10: "boy",
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11: "girl",
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},
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"name": {
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0: "Amy",
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1: "Edward",
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2: "Sophia",
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3: "Amy",
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4: "Cindy",
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5: "Dawn",
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6: "Edward",
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7: "Sophia",
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8: "Dawn",
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9: "Tony",
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10: "Tony",
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11: "Cindy",
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},
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"SUM(num)": {
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0: 45426,
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1: 31290,
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2: 18859,
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3: 14740,
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4: 14149,
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5: 11403,
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6: 9395,
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7: 7181,
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8: 5089,
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9: 3765,
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10: 2673,
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11: 1218,
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},
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"MAX(num)": {
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0: 2227,
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1: 1280,
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2: 2588,
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3: 854,
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4: 842,
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5: 1157,
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6: 389,
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7: 1187,
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8: 461,
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9: 598,
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10: 247,
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11: 217,
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},
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}
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)
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assert (
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df.to_markdown()
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== """
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| | state | gender | name | SUM(num) | MAX(num) |
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|---:|:--------|:---------|:-------|-----------:|-----------:|
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| 0 | CA | girl | Amy | 45426 | 2227 |
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| 1 | CA | boy | Edward | 31290 | 1280 |
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| 2 | CA | girl | Sophia | 18859 | 2588 |
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| 3 | FL | girl | Amy | 14740 | 854 |
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| 4 | CA | girl | Cindy | 14149 | 842 |
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| 5 | CA | girl | Dawn | 11403 | 1157 |
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| 6 | FL | boy | Edward | 9395 | 389 |
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| 7 | FL | girl | Sophia | 7181 | 1187 |
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| 8 | FL | girl | Dawn | 5089 | 461 |
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| 9 | CA | boy | Tony | 3765 | 598 |
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| 10 | FL | boy | Tony | 2673 | 247 |
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| 11 | FL | girl | Cindy | 1218 | 217 |
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""".strip()
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)
|
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pivoted = pivot_df(
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df,
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rows=["gender", "name"],
|
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columns=["state"],
|
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metrics=["SUM(num)", "MAX(num)"],
|
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aggfunc="Sum",
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transpose_pivot=False,
|
|
combine_metrics=False,
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show_rows_total=False,
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show_columns_total=False,
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apply_metrics_on_rows=False,
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)
|
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assert (
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pivoted.to_markdown()
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== """
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| | ('SUM(num)', 'CA') | ('SUM(num)', 'FL') | ('MAX(num)', 'CA') | ('MAX(num)', 'FL') |
|
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|:-------------------|---------------------:|---------------------:|---------------------:|---------------------:|
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| ('boy', 'Edward') | 31290 | 9395 | 1280 | 389 |
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| ('boy', 'Tony') | 3765 | 2673 | 598 | 247 |
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| ('girl', 'Amy') | 45426 | 14740 | 2227 | 854 |
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| ('girl', 'Cindy') | 14149 | 1218 | 842 | 217 |
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| ('girl', 'Dawn') | 11403 | 5089 | 1157 | 461 |
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| ('girl', 'Sophia') | 18859 | 7181 | 2588 | 1187 |
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""".strip()
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)
|
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|
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# transpose_pivot
|
|
pivoted = pivot_df(
|
|
df,
|
|
rows=["gender", "name"],
|
|
columns=["state"],
|
|
metrics=["SUM(num)", "MAX(num)"],
|
|
aggfunc="Sum",
|
|
transpose_pivot=True,
|
|
combine_metrics=False,
|
|
show_rows_total=False,
|
|
show_columns_total=False,
|
|
apply_metrics_on_rows=False,
|
|
)
|
|
assert (
|
|
pivoted.to_markdown()
|
|
== """
|
|
| | ('SUM(num)', 'boy', 'Edward') | ('SUM(num)', 'boy', 'Tony') | ('SUM(num)', 'girl', 'Amy') | ('SUM(num)', 'girl', 'Cindy') | ('SUM(num)', 'girl', 'Dawn') | ('SUM(num)', 'girl', 'Sophia') | ('MAX(num)', 'boy', 'Edward') | ('MAX(num)', 'boy', 'Tony') | ('MAX(num)', 'girl', 'Amy') | ('MAX(num)', 'girl', 'Cindy') | ('MAX(num)', 'girl', 'Dawn') | ('MAX(num)', 'girl', 'Sophia') |
|
|
|:--------|--------------------------------:|------------------------------:|------------------------------:|--------------------------------:|-------------------------------:|---------------------------------:|--------------------------------:|------------------------------:|------------------------------:|--------------------------------:|-------------------------------:|---------------------------------:|
|
|
| ('CA',) | 31290 | 3765 | 45426 | 14149 | 11403 | 18859 | 1280 | 598 | 2227 | 842 | 1157 | 2588 |
|
|
| ('FL',) | 9395 | 2673 | 14740 | 1218 | 5089 | 7181 | 389 | 247 | 854 | 217 | 461 | 1187 |
|
|
""".strip()
|
|
)
|
|
|
|
# combine_metrics
|
|
pivoted = pivot_df(
|
|
df,
|
|
rows=["gender", "name"],
|
|
columns=["state"],
|
|
metrics=["SUM(num)", "MAX(num)"],
|
|
aggfunc="Sum",
|
|
transpose_pivot=False,
|
|
combine_metrics=True,
|
|
show_rows_total=False,
|
|
show_columns_total=False,
|
|
apply_metrics_on_rows=False,
|
|
)
|
|
assert (
|
|
pivoted.to_markdown()
|
|
== """
|
|
| | ('CA', 'SUM(num)') | ('CA', 'MAX(num)') | ('FL', 'SUM(num)') | ('FL', 'MAX(num)') |
|
|
|:-------------------|---------------------:|---------------------:|---------------------:|---------------------:|
|
|
| ('boy', 'Edward') | 31290 | 1280 | 9395 | 389 |
|
|
| ('boy', 'Tony') | 3765 | 598 | 2673 | 247 |
|
|
| ('girl', 'Amy') | 45426 | 2227 | 14740 | 854 |
|
|
| ('girl', 'Cindy') | 14149 | 842 | 1218 | 217 |
|
|
| ('girl', 'Dawn') | 11403 | 1157 | 5089 | 461 |
|
|
| ('girl', 'Sophia') | 18859 | 2588 | 7181 | 1187 |
|
|
""".strip()
|
|
)
|
|
|
|
# show totals
|
|
pivoted = pivot_df(
|
|
df,
|
|
rows=["gender", "name"],
|
|
columns=["state"],
|
|
metrics=["SUM(num)", "MAX(num)"],
|
|
aggfunc="Sum",
|
|
transpose_pivot=False,
|
|
combine_metrics=False,
|
|
show_rows_total=True,
|
|
show_columns_total=True,
|
|
apply_metrics_on_rows=False,
|
|
)
|
|
assert (
|
|
pivoted.to_markdown()
|
|
== """
|
|
| | ('SUM(num)', 'CA') | ('SUM(num)', 'FL') | ('SUM(num)', 'Subtotal') | ('MAX(num)', 'CA') | ('MAX(num)', 'FL') | ('MAX(num)', 'Subtotal') | ('Total (Sum)', '') |
|
|
|:---------------------|---------------------:|---------------------:|---------------------------:|---------------------:|---------------------:|---------------------------:|----------------------:|
|
|
| ('boy', 'Edward') | 31290 | 9395 | 40685 | 1280 | 389 | 1669 | 42354 |
|
|
| ('boy', 'Tony') | 3765 | 2673 | 6438 | 598 | 247 | 845 | 7283 |
|
|
| ('boy', 'Subtotal') | 35055 | 12068 | 47123 | 1878 | 636 | 2514 | 49637 |
|
|
| ('girl', 'Amy') | 45426 | 14740 | 60166 | 2227 | 854 | 3081 | 63247 |
|
|
| ('girl', 'Cindy') | 14149 | 1218 | 15367 | 842 | 217 | 1059 | 16426 |
|
|
| ('girl', 'Dawn') | 11403 | 5089 | 16492 | 1157 | 461 | 1618 | 18110 |
|
|
| ('girl', 'Sophia') | 18859 | 7181 | 26040 | 2588 | 1187 | 3775 | 29815 |
|
|
| ('girl', 'Subtotal') | 89837 | 28228 | 118065 | 6814 | 2719 | 9533 | 127598 |
|
|
| ('Total (Sum)', '') | 124892 | 40296 | 165188 | 8692 | 3355 | 12047 | 177235 |
|
|
""".strip()
|
|
)
|
|
|
|
# apply_metrics_on_rows
|
|
pivoted = pivot_df(
|
|
df,
|
|
rows=["gender", "name"],
|
|
columns=["state"],
|
|
metrics=["SUM(num)", "MAX(num)"],
|
|
aggfunc="Sum",
|
|
transpose_pivot=False,
|
|
combine_metrics=False,
|
|
show_rows_total=False,
|
|
show_columns_total=False,
|
|
apply_metrics_on_rows=True,
|
|
)
|
|
assert (
|
|
pivoted.to_markdown()
|
|
== """
|
|
| | ('CA',) | ('FL',) |
|
|
|:-------------------------------|----------:|----------:|
|
|
| ('SUM(num)', 'boy', 'Edward') | 31290 | 9395 |
|
|
| ('SUM(num)', 'boy', 'Tony') | 3765 | 2673 |
|
|
| ('SUM(num)', 'girl', 'Amy') | 45426 | 14740 |
|
|
| ('SUM(num)', 'girl', 'Cindy') | 14149 | 1218 |
|
|
| ('SUM(num)', 'girl', 'Dawn') | 11403 | 5089 |
|
|
| ('SUM(num)', 'girl', 'Sophia') | 18859 | 7181 |
|
|
| ('MAX(num)', 'boy', 'Edward') | 1280 | 389 |
|
|
| ('MAX(num)', 'boy', 'Tony') | 598 | 247 |
|
|
| ('MAX(num)', 'girl', 'Amy') | 2227 | 854 |
|
|
| ('MAX(num)', 'girl', 'Cindy') | 842 | 217 |
|
|
| ('MAX(num)', 'girl', 'Dawn') | 1157 | 461 |
|
|
| ('MAX(num)', 'girl', 'Sophia') | 2588 | 1187 |
|
|
""".strip()
|
|
)
|
|
|
|
# apply_metrics_on_rows with combine_metrics
|
|
pivoted = pivot_df(
|
|
df,
|
|
rows=["gender", "name"],
|
|
columns=["state"],
|
|
metrics=["SUM(num)", "MAX(num)"],
|
|
aggfunc="Sum",
|
|
transpose_pivot=False,
|
|
combine_metrics=True,
|
|
show_rows_total=False,
|
|
show_columns_total=False,
|
|
apply_metrics_on_rows=True,
|
|
)
|
|
assert (
|
|
pivoted.to_markdown()
|
|
== """
|
|
| | ('CA',) | ('FL',) |
|
|
|:-------------------------------|----------:|----------:|
|
|
| ('boy', 'Edward', 'SUM(num)') | 31290 | 9395 |
|
|
| ('boy', 'Edward', 'MAX(num)') | 1280 | 389 |
|
|
| ('boy', 'Tony', 'SUM(num)') | 3765 | 2673 |
|
|
| ('boy', 'Tony', 'MAX(num)') | 598 | 247 |
|
|
| ('girl', 'Amy', 'SUM(num)') | 45426 | 14740 |
|
|
| ('girl', 'Amy', 'MAX(num)') | 2227 | 854 |
|
|
| ('girl', 'Cindy', 'SUM(num)') | 14149 | 1218 |
|
|
| ('girl', 'Cindy', 'MAX(num)') | 842 | 217 |
|
|
| ('girl', 'Dawn', 'SUM(num)') | 11403 | 5089 |
|
|
| ('girl', 'Dawn', 'MAX(num)') | 1157 | 461 |
|
|
| ('girl', 'Sophia', 'SUM(num)') | 18859 | 7181 |
|
|
| ('girl', 'Sophia', 'MAX(num)') | 2588 | 1187 |
|
|
""".strip()
|
|
)
|
|
|
|
# everything
|
|
pivoted = pivot_df(
|
|
df,
|
|
rows=["gender", "name"],
|
|
columns=["state"],
|
|
metrics=["SUM(num)", "MAX(num)"],
|
|
aggfunc="Sum",
|
|
transpose_pivot=True,
|
|
combine_metrics=True,
|
|
show_rows_total=True,
|
|
show_columns_total=True,
|
|
apply_metrics_on_rows=True,
|
|
)
|
|
assert (
|
|
pivoted.to_markdown()
|
|
== """
|
|
| | ('boy', 'Edward') | ('boy', 'Tony') | ('boy', 'Subtotal') | ('girl', 'Amy') | ('girl', 'Cindy') | ('girl', 'Dawn') | ('girl', 'Sophia') | ('girl', 'Subtotal') | ('Total (Sum)', '') |
|
|
|:--------------------|--------------------:|------------------:|----------------------:|------------------:|--------------------:|-------------------:|---------------------:|-----------------------:|----------------------:|
|
|
| ('CA', 'SUM(num)') | 31290 | 3765 | 35055 | 45426 | 14149 | 11403 | 18859 | 89837 | 124892 |
|
|
| ('CA', 'MAX(num)') | 1280 | 598 | 1878 | 2227 | 842 | 1157 | 2588 | 6814 | 8692 |
|
|
| ('CA', 'Subtotal') | 32570 | 4363 | 36933 | 47653 | 14991 | 12560 | 21447 | 96651 | 133584 |
|
|
| ('FL', 'SUM(num)') | 9395 | 2673 | 12068 | 14740 | 1218 | 5089 | 7181 | 28228 | 40296 |
|
|
| ('FL', 'MAX(num)') | 389 | 247 | 636 | 854 | 217 | 461 | 1187 | 2719 | 3355 |
|
|
| ('FL', 'Subtotal') | 9784 | 2920 | 12704 | 15594 | 1435 | 5550 | 8368 | 30947 | 43651 |
|
|
| ('Total (Sum)', '') | 42354 | 7283 | 49637 | 63247 | 16426 | 18110 | 29815 | 127598 | 177235 |
|
|
""".strip()
|
|
)
|
|
|
|
# fraction
|
|
pivoted = pivot_df(
|
|
df,
|
|
rows=["gender", "name"],
|
|
columns=["state"],
|
|
metrics=["SUM(num)", "MAX(num)"],
|
|
aggfunc="Sum as Fraction of Columns",
|
|
transpose_pivot=False,
|
|
combine_metrics=False,
|
|
show_rows_total=False,
|
|
show_columns_total=True,
|
|
apply_metrics_on_rows=False,
|
|
)
|
|
assert (
|
|
pivoted.to_markdown()
|
|
== """
|
|
| | ('SUM(num)', 'CA') | ('SUM(num)', 'FL') | ('MAX(num)', 'CA') | ('MAX(num)', 'FL') |
|
|
|:-------------------------------------------|---------------------:|---------------------:|---------------------:|---------------------:|
|
|
| ('boy', 'Edward') | 0.250536 | 0.23315 | 0.147262 | 0.115946 |
|
|
| ('boy', 'Tony') | 0.030146 | 0.0663341 | 0.0687989 | 0.0736215 |
|
|
| ('boy', 'Subtotal') | 0.280683 | 0.299484 | 0.216061 | 0.189568 |
|
|
| ('girl', 'Amy') | 0.363722 | 0.365793 | 0.256213 | 0.254545 |
|
|
| ('girl', 'Cindy') | 0.11329 | 0.0302263 | 0.0968707 | 0.0646796 |
|
|
| ('girl', 'Dawn') | 0.0913029 | 0.12629 | 0.133111 | 0.137407 |
|
|
| ('girl', 'Sophia') | 0.151002 | 0.178206 | 0.297745 | 0.3538 |
|
|
| ('girl', 'Subtotal') | 0.719317 | 0.700516 | 0.783939 | 0.810432 |
|
|
| ('Total (Sum as Fraction of Columns)', '') | 1 | 1 | 1 | 1 |
|
|
""".strip()
|
|
)
|
|
|
|
|
|
def test_pivot_df_multi_column():
|
|
"""
|
|
Pivot table when 2 columns, no rows and 2 metrics are selected.
|
|
"""
|
|
df = pd.DataFrame.from_dict(
|
|
{
|
|
"state": {
|
|
0: "CA",
|
|
1: "CA",
|
|
2: "CA",
|
|
3: "FL",
|
|
4: "CA",
|
|
5: "CA",
|
|
6: "FL",
|
|
7: "FL",
|
|
8: "FL",
|
|
9: "CA",
|
|
10: "FL",
|
|
11: "FL",
|
|
},
|
|
"gender": {
|
|
0: "girl",
|
|
1: "boy",
|
|
2: "girl",
|
|
3: "girl",
|
|
4: "girl",
|
|
5: "girl",
|
|
6: "boy",
|
|
7: "girl",
|
|
8: "girl",
|
|
9: "boy",
|
|
10: "boy",
|
|
11: "girl",
|
|
},
|
|
"SUM(num)": {
|
|
0: 45426,
|
|
1: 31290,
|
|
2: 18859,
|
|
3: 14740,
|
|
4: 14149,
|
|
5: 11403,
|
|
6: 9395,
|
|
7: 7181,
|
|
8: 5089,
|
|
9: 3765,
|
|
10: 2673,
|
|
11: 1218,
|
|
},
|
|
"MAX(num)": {
|
|
0: 2227,
|
|
1: 1280,
|
|
2: 2588,
|
|
3: 854,
|
|
4: 842,
|
|
5: 1157,
|
|
6: 389,
|
|
7: 1187,
|
|
8: 461,
|
|
9: 598,
|
|
10: 247,
|
|
11: 217,
|
|
},
|
|
}
|
|
)
|
|
|
|
pivoted = pivot_df(
|
|
df,
|
|
rows=None,
|
|
columns=["state", "gender"],
|
|
metrics=["SUM(num)", "MAX(num)"],
|
|
aggfunc="Sum",
|
|
transpose_pivot=False,
|
|
combine_metrics=False,
|
|
show_rows_total=False,
|
|
show_columns_total=False,
|
|
apply_metrics_on_rows=False,
|
|
)
|
|
assert (
|
|
pivoted.to_markdown()
|
|
== """
|
|
| | ('SUM(num)', 'boy') | ('SUM(num)', 'girl') | ('MAX(num)', 'boy') | ('MAX(num)', 'girl') |
|
|
|:-----------------|----------------------:|-----------------------:|----------------------:|-----------------------:|
|
|
| ('CA',) | 35055 | 89837 | 1878 | 6814 |
|
|
| ('Total (Sum)',) | 12068 | 28228 | 636 | 2719 |
|
|
""".strip()
|
|
)
|
|
|
|
# transpose_pivot
|
|
pivoted = pivot_df(
|
|
df,
|
|
rows=None,
|
|
columns=["state", "gender"],
|
|
metrics=["SUM(num)", "MAX(num)"],
|
|
aggfunc="Sum",
|
|
transpose_pivot=True,
|
|
combine_metrics=False,
|
|
show_rows_total=False,
|
|
show_columns_total=False,
|
|
apply_metrics_on_rows=False,
|
|
)
|
|
assert (
|
|
pivoted.to_markdown()
|
|
== """
|
|
| | ('SUM(num)',) | ('MAX(num)',) |
|
|
|:---------------|----------------:|----------------:|
|
|
| ('CA', 'boy') | 35055 | 1878 |
|
|
| ('CA', 'girl') | 89837 | 6814 |
|
|
| ('FL', 'boy') | 12068 | 636 |
|
|
| ('FL', 'girl') | 28228 | 2719 |
|
|
""".strip()
|
|
)
|
|
|
|
# combine_metrics
|
|
pivoted = pivot_df(
|
|
df,
|
|
rows=None,
|
|
columns=["state", "gender"],
|
|
metrics=["SUM(num)", "MAX(num)"],
|
|
aggfunc="Sum",
|
|
transpose_pivot=False,
|
|
combine_metrics=True,
|
|
show_rows_total=False,
|
|
show_columns_total=False,
|
|
apply_metrics_on_rows=False,
|
|
)
|
|
assert (
|
|
pivoted.to_markdown()
|
|
== """
|
|
| | ('boy', 'SUM(num)') | ('boy', 'MAX(num)') | ('girl', 'SUM(num)') | ('girl', 'MAX(num)') |
|
|
|:-----------------|----------------------:|----------------------:|-----------------------:|-----------------------:|
|
|
| ('CA',) | 35055 | 1878 | 89837 | 6814 |
|
|
| ('Total (Sum)',) | 12068 | 636 | 28228 | 2719 |
|
|
""".strip()
|
|
)
|
|
|
|
# show totals
|
|
pivoted = pivot_df(
|
|
df,
|
|
rows=None,
|
|
columns=["state", "gender"],
|
|
metrics=["SUM(num)", "MAX(num)"],
|
|
aggfunc="Sum",
|
|
transpose_pivot=False,
|
|
combine_metrics=False,
|
|
show_rows_total=True,
|
|
show_columns_total=True,
|
|
apply_metrics_on_rows=False,
|
|
)
|
|
assert (
|
|
pivoted.to_markdown()
|
|
== """
|
|
| | ('SUM(num)', 'boy') | ('SUM(num)', 'girl') | ('SUM(num)', 'Subtotal') | ('MAX(num)', 'boy') | ('MAX(num)', 'girl') | ('MAX(num)', 'Subtotal') | ('Total (Sum)', '') |
|
|
|:-----------------|----------------------:|-----------------------:|---------------------------:|----------------------:|-----------------------:|---------------------------:|----------------------:|
|
|
| ('CA',) | 35055 | 89837 | 124892 | 1878 | 6814 | 8692 | 133584 |
|
|
| ('Total (Sum)',) | 12068 | 28228 | 40296 | 636 | 2719 | 3355 | 43651 |
|
|
|
|
""".strip()
|
|
)
|
|
|
|
# apply_metrics_on_rows
|
|
pivoted = pivot_df(
|
|
df,
|
|
rows=None,
|
|
columns=["state", "gender"],
|
|
metrics=["SUM(num)", "MAX(num)"],
|
|
aggfunc="Sum",
|
|
transpose_pivot=False,
|
|
combine_metrics=False,
|
|
show_rows_total=False,
|
|
show_columns_total=False,
|
|
apply_metrics_on_rows=True,
|
|
)
|
|
assert (
|
|
pivoted.to_markdown()
|
|
== """
|
|
| | ('CA', 'boy') | ('CA', 'girl') | ('FL', 'boy') | ('FL', 'girl') |
|
|
|:--------------|----------------:|-----------------:|----------------:|-----------------:|
|
|
| ('SUM(num)',) | 35055 | 89837 | 12068 | 28228 |
|
|
| ('MAX(num)',) | 1878 | 6814 | 636 | 2719 |
|
|
""".strip()
|
|
)
|
|
|
|
# apply_metrics_on_rows with combine_metrics
|
|
pivoted = pivot_df(
|
|
df,
|
|
rows=None,
|
|
columns=["state", "gender"],
|
|
metrics=["SUM(num)", "MAX(num)"],
|
|
aggfunc="Sum",
|
|
transpose_pivot=False,
|
|
combine_metrics=True,
|
|
show_rows_total=False,
|
|
show_columns_total=False,
|
|
apply_metrics_on_rows=True,
|
|
)
|
|
assert (
|
|
pivoted.to_markdown()
|
|
== """
|
|
| | ('CA', 'boy') | ('CA', 'girl') | ('FL', 'boy') | ('FL', 'girl') |
|
|
|:--------------|----------------:|-----------------:|----------------:|-----------------:|
|
|
| ('SUM(num)',) | 35055 | 89837 | 12068 | 28228 |
|
|
| ('MAX(num)',) | 1878 | 6814 | 636 | 2719 |
|
|
""".strip()
|
|
)
|
|
|
|
# everything
|
|
pivoted = pivot_df(
|
|
df,
|
|
rows=None,
|
|
columns=["state", "gender"],
|
|
metrics=["SUM(num)", "MAX(num)"],
|
|
aggfunc="Sum",
|
|
transpose_pivot=True,
|
|
combine_metrics=True,
|
|
show_rows_total=True,
|
|
show_columns_total=True,
|
|
apply_metrics_on_rows=True,
|
|
)
|
|
assert (
|
|
pivoted.to_markdown()
|
|
== """
|
|
| | ('CA',) | ('Total (Sum)',) |
|
|
|:---------------------|----------:|-------------------:|
|
|
| ('boy', 'SUM(num)') | 35055 | 12068 |
|
|
| ('boy', 'MAX(num)') | 1878 | 636 |
|
|
| ('boy', 'Subtotal') | 36933 | 12704 |
|
|
| ('girl', 'SUM(num)') | 89837 | 28228 |
|
|
| ('girl', 'MAX(num)') | 6814 | 2719 |
|
|
| ('girl', 'Subtotal') | 96651 | 30947 |
|
|
| ('Total (Sum)', '') | 133584 | 43651 |
|
|
""".strip()
|
|
)
|
|
|
|
# fraction
|
|
pivoted = pivot_df(
|
|
df,
|
|
rows=None,
|
|
columns=["state", "gender"],
|
|
metrics=["SUM(num)", "MAX(num)"],
|
|
aggfunc="Sum as Fraction of Columns",
|
|
transpose_pivot=False,
|
|
combine_metrics=False,
|
|
show_rows_total=False,
|
|
show_columns_total=True,
|
|
apply_metrics_on_rows=False,
|
|
)
|
|
assert (
|
|
pivoted.to_markdown()
|
|
== """
|
|
| | ('SUM(num)', 'boy') | ('SUM(num)', 'girl') | ('MAX(num)', 'boy') | ('MAX(num)', 'girl') |
|
|
|:----------------------------------------|----------------------:|-----------------------:|----------------------:|-----------------------:|
|
|
| ('CA',) | 0.743904 | 0.760911 | 0.747017 | 0.71478 |
|
|
| ('Total (Sum as Fraction of Columns)',) | 0.256096 | 0.239089 | 0.252983 | 0.28522 |
|
|
""".strip()
|
|
)
|
|
|
|
|
|
def test_pivot_df_complex_null_values():
|
|
"""
|
|
Pivot table when a column, rows and 2 metrics are selected.
|
|
"""
|
|
df = pd.DataFrame.from_dict(
|
|
{
|
|
"state": {
|
|
0: None,
|
|
1: None,
|
|
2: None,
|
|
3: None,
|
|
4: None,
|
|
5: None,
|
|
6: None,
|
|
7: None,
|
|
8: None,
|
|
9: None,
|
|
10: None,
|
|
11: None,
|
|
},
|
|
"gender": {
|
|
0: "girl",
|
|
1: "boy",
|
|
2: "girl",
|
|
3: "girl",
|
|
4: "girl",
|
|
5: "girl",
|
|
6: "boy",
|
|
7: "girl",
|
|
8: "girl",
|
|
9: "boy",
|
|
10: "boy",
|
|
11: "girl",
|
|
},
|
|
"name": {
|
|
0: "Amy",
|
|
1: "Edward",
|
|
2: "Sophia",
|
|
3: "Amy",
|
|
4: "Cindy",
|
|
5: "Dawn",
|
|
6: "Edward",
|
|
7: "Sophia",
|
|
8: "Dawn",
|
|
9: "Tony",
|
|
10: "Tony",
|
|
11: "Cindy",
|
|
},
|
|
"SUM(num)": {
|
|
0: 45426,
|
|
1: 31290,
|
|
2: 18859,
|
|
3: 14740,
|
|
4: 14149,
|
|
5: 11403,
|
|
6: 9395,
|
|
7: 7181,
|
|
8: 5089,
|
|
9: 3765,
|
|
10: 2673,
|
|
11: 1218,
|
|
},
|
|
"MAX(num)": {
|
|
0: 2227,
|
|
1: 1280,
|
|
2: 2588,
|
|
3: 854,
|
|
4: 842,
|
|
5: 1157,
|
|
6: 389,
|
|
7: 1187,
|
|
8: 461,
|
|
9: 598,
|
|
10: 247,
|
|
11: 217,
|
|
},
|
|
}
|
|
)
|
|
assert (
|
|
df.to_markdown()
|
|
== """
|
|
| | state | gender | name | SUM(num) | MAX(num) |
|
|
|---:|:--------|:---------|:-------|-----------:|-----------:|
|
|
| 0 | | girl | Amy | 45426 | 2227 |
|
|
| 1 | | boy | Edward | 31290 | 1280 |
|
|
| 2 | | girl | Sophia | 18859 | 2588 |
|
|
| 3 | | girl | Amy | 14740 | 854 |
|
|
| 4 | | girl | Cindy | 14149 | 842 |
|
|
| 5 | | girl | Dawn | 11403 | 1157 |
|
|
| 6 | | boy | Edward | 9395 | 389 |
|
|
| 7 | | girl | Sophia | 7181 | 1187 |
|
|
| 8 | | girl | Dawn | 5089 | 461 |
|
|
| 9 | | boy | Tony | 3765 | 598 |
|
|
| 10 | | boy | Tony | 2673 | 247 |
|
|
| 11 | | girl | Cindy | 1218 | 217 |
|
|
""".strip()
|
|
)
|
|
|
|
pivoted = pivot_df(
|
|
df,
|
|
rows=["gender", "name"],
|
|
columns=["state"],
|
|
metrics=["SUM(num)", "MAX(num)"],
|
|
aggfunc="Sum",
|
|
transpose_pivot=False,
|
|
combine_metrics=False,
|
|
show_rows_total=False,
|
|
show_columns_total=False,
|
|
apply_metrics_on_rows=False,
|
|
)
|
|
assert (
|
|
pivoted.to_markdown()
|
|
== """
|
|
| | ('SUM(num)', 'NULL') | ('MAX(num)', 'NULL') |
|
|
|:-------------------|-----------------------:|-----------------------:|
|
|
| ('boy', 'Edward') | 40685 | 1669 |
|
|
| ('boy', 'Tony') | 6438 | 845 |
|
|
| ('girl', 'Amy') | 60166 | 3081 |
|
|
| ('girl', 'Cindy') | 15367 | 1059 |
|
|
| ('girl', 'Dawn') | 16492 | 1618 |
|
|
| ('girl', 'Sophia') | 26040 | 3775 |
|
|
|
|
""".strip()
|
|
)
|
|
|
|
# transpose_pivot
|
|
pivoted = pivot_df(
|
|
df,
|
|
rows=["gender", "name"],
|
|
columns=["state"],
|
|
metrics=["SUM(num)", "MAX(num)"],
|
|
aggfunc="Sum",
|
|
transpose_pivot=True,
|
|
combine_metrics=False,
|
|
show_rows_total=False,
|
|
show_columns_total=False,
|
|
apply_metrics_on_rows=False,
|
|
)
|
|
assert (
|
|
pivoted.to_markdown()
|
|
== """
|
|
| | ('SUM(num)', 'boy', 'Edward') | ('SUM(num)', 'boy', 'Tony') | ('SUM(num)', 'girl', 'Amy') | ('SUM(num)', 'girl', 'Cindy') | ('SUM(num)', 'girl', 'Dawn') | ('SUM(num)', 'girl', 'Sophia') | ('MAX(num)', 'boy', 'Edward') | ('MAX(num)', 'boy', 'Tony') | ('MAX(num)', 'girl', 'Amy') | ('MAX(num)', 'girl', 'Cindy') | ('MAX(num)', 'girl', 'Dawn') | ('MAX(num)', 'girl', 'Sophia') |
|
|
|:----------|--------------------------------:|------------------------------:|------------------------------:|--------------------------------:|-------------------------------:|---------------------------------:|--------------------------------:|------------------------------:|------------------------------:|--------------------------------:|-------------------------------:|---------------------------------:|
|
|
| ('NULL',) | 40685 | 6438 | 60166 | 15367 | 16492 | 26040 | 1669 | 845 | 3081 | 1059 | 1618 | 3775 |
|
|
""".strip()
|
|
)
|
|
|
|
# combine_metrics
|
|
pivoted = pivot_df(
|
|
df,
|
|
rows=["gender", "name"],
|
|
columns=["state"],
|
|
metrics=["SUM(num)", "MAX(num)"],
|
|
aggfunc="Sum",
|
|
transpose_pivot=False,
|
|
combine_metrics=True,
|
|
show_rows_total=False,
|
|
show_columns_total=False,
|
|
apply_metrics_on_rows=False,
|
|
)
|
|
assert (
|
|
pivoted.to_markdown()
|
|
== """
|
|
| | ('NULL', 'SUM(num)') | ('NULL', 'MAX(num)') |
|
|
|:-------------------|-----------------------:|-----------------------:|
|
|
| ('boy', 'Edward') | 40685 | 1669 |
|
|
| ('boy', 'Tony') | 6438 | 845 |
|
|
| ('girl', 'Amy') | 60166 | 3081 |
|
|
| ('girl', 'Cindy') | 15367 | 1059 |
|
|
| ('girl', 'Dawn') | 16492 | 1618 |
|
|
| ('girl', 'Sophia') | 26040 | 3775 |
|
|
""".strip()
|
|
)
|
|
|
|
# show totals
|
|
pivoted = pivot_df(
|
|
df,
|
|
rows=["gender", "name"],
|
|
columns=["state"],
|
|
metrics=["SUM(num)", "MAX(num)"],
|
|
aggfunc="Sum",
|
|
transpose_pivot=False,
|
|
combine_metrics=False,
|
|
show_rows_total=True,
|
|
show_columns_total=True,
|
|
apply_metrics_on_rows=False,
|
|
)
|
|
assert (
|
|
pivoted.to_markdown()
|
|
== """
|
|
| | ('SUM(num)', 'NULL') | ('SUM(num)', 'Subtotal') | ('MAX(num)', 'NULL') | ('MAX(num)', 'Subtotal') | ('Total (Sum)', '') |
|
|
|:---------------------|-----------------------:|---------------------------:|-----------------------:|---------------------------:|----------------------:|
|
|
| ('boy', 'Edward') | 40685 | 40685 | 1669 | 1669 | 42354 |
|
|
| ('boy', 'Tony') | 6438 | 6438 | 845 | 845 | 7283 |
|
|
| ('boy', 'Subtotal') | 47123 | 47123 | 2514 | 2514 | 49637 |
|
|
| ('girl', 'Amy') | 60166 | 60166 | 3081 | 3081 | 63247 |
|
|
| ('girl', 'Cindy') | 15367 | 15367 | 1059 | 1059 | 16426 |
|
|
| ('girl', 'Dawn') | 16492 | 16492 | 1618 | 1618 | 18110 |
|
|
| ('girl', 'Sophia') | 26040 | 26040 | 3775 | 3775 | 29815 |
|
|
| ('girl', 'Subtotal') | 118065 | 118065 | 9533 | 9533 | 127598 |
|
|
| ('Total (Sum)', '') | 165188 | 165188 | 12047 | 12047 | 177235 |
|
|
""".strip()
|
|
)
|
|
|
|
# apply_metrics_on_rows
|
|
pivoted = pivot_df(
|
|
df,
|
|
rows=["gender", "name"],
|
|
columns=["state"],
|
|
metrics=["SUM(num)", "MAX(num)"],
|
|
aggfunc="Sum",
|
|
transpose_pivot=False,
|
|
combine_metrics=False,
|
|
show_rows_total=False,
|
|
show_columns_total=False,
|
|
apply_metrics_on_rows=True,
|
|
)
|
|
assert (
|
|
pivoted.to_markdown()
|
|
== """
|
|
| | ('NULL',) |
|
|
|:-------------------------------|------------:|
|
|
| ('SUM(num)', 'boy', 'Edward') | 40685 |
|
|
| ('SUM(num)', 'boy', 'Tony') | 6438 |
|
|
| ('SUM(num)', 'girl', 'Amy') | 60166 |
|
|
| ('SUM(num)', 'girl', 'Cindy') | 15367 |
|
|
| ('SUM(num)', 'girl', 'Dawn') | 16492 |
|
|
| ('SUM(num)', 'girl', 'Sophia') | 26040 |
|
|
| ('MAX(num)', 'boy', 'Edward') | 1669 |
|
|
| ('MAX(num)', 'boy', 'Tony') | 845 |
|
|
| ('MAX(num)', 'girl', 'Amy') | 3081 |
|
|
| ('MAX(num)', 'girl', 'Cindy') | 1059 |
|
|
| ('MAX(num)', 'girl', 'Dawn') | 1618 |
|
|
| ('MAX(num)', 'girl', 'Sophia') | 3775 |
|
|
""".strip()
|
|
)
|
|
|
|
# apply_metrics_on_rows with combine_metrics
|
|
pivoted = pivot_df(
|
|
df,
|
|
rows=["gender", "name"],
|
|
columns=["state"],
|
|
metrics=["SUM(num)", "MAX(num)"],
|
|
aggfunc="Sum",
|
|
transpose_pivot=False,
|
|
combine_metrics=True,
|
|
show_rows_total=False,
|
|
show_columns_total=False,
|
|
apply_metrics_on_rows=True,
|
|
)
|
|
assert (
|
|
pivoted.to_markdown()
|
|
== """
|
|
| | ('NULL',) |
|
|
|:-------------------------------|------------:|
|
|
| ('boy', 'Edward', 'SUM(num)') | 40685 |
|
|
| ('boy', 'Edward', 'MAX(num)') | 1669 |
|
|
| ('boy', 'Tony', 'SUM(num)') | 6438 |
|
|
| ('boy', 'Tony', 'MAX(num)') | 845 |
|
|
| ('girl', 'Amy', 'SUM(num)') | 60166 |
|
|
| ('girl', 'Amy', 'MAX(num)') | 3081 |
|
|
| ('girl', 'Cindy', 'SUM(num)') | 15367 |
|
|
| ('girl', 'Cindy', 'MAX(num)') | 1059 |
|
|
| ('girl', 'Dawn', 'SUM(num)') | 16492 |
|
|
| ('girl', 'Dawn', 'MAX(num)') | 1618 |
|
|
| ('girl', 'Sophia', 'SUM(num)') | 26040 |
|
|
| ('girl', 'Sophia', 'MAX(num)') | 3775 |
|
|
""".strip()
|
|
)
|
|
|
|
# everything
|
|
pivoted = pivot_df(
|
|
df,
|
|
rows=["gender", "name"],
|
|
columns=["state"],
|
|
metrics=["SUM(num)", "MAX(num)"],
|
|
aggfunc="Sum",
|
|
transpose_pivot=True,
|
|
combine_metrics=True,
|
|
show_rows_total=True,
|
|
show_columns_total=True,
|
|
apply_metrics_on_rows=True,
|
|
)
|
|
assert (
|
|
pivoted.to_markdown()
|
|
== """
|
|
| | ('boy', 'Edward') | ('boy', 'Tony') | ('boy', 'Subtotal') | ('girl', 'Amy') | ('girl', 'Cindy') | ('girl', 'Dawn') | ('girl', 'Sophia') | ('girl', 'Subtotal') | ('Total (Sum)', '') |
|
|
|:---------------------|--------------------:|------------------:|----------------------:|------------------:|--------------------:|-------------------:|---------------------:|-----------------------:|----------------------:|
|
|
| ('NULL', 'SUM(num)') | 40685 | 6438 | 47123 | 60166 | 15367 | 16492 | 26040 | 118065 | 165188 |
|
|
| ('NULL', 'MAX(num)') | 1669 | 845 | 2514 | 3081 | 1059 | 1618 | 3775 | 9533 | 12047 |
|
|
| ('NULL', 'Subtotal') | 42354 | 7283 | 49637 | 63247 | 16426 | 18110 | 29815 | 127598 | 177235 |
|
|
| ('Total (Sum)', '') | 42354 | 7283 | 49637 | 63247 | 16426 | 18110 | 29815 | 127598 | 177235 |
|
|
""".strip()
|
|
)
|
|
|
|
# fraction
|
|
pivoted = pivot_df(
|
|
df,
|
|
rows=["gender", "name"],
|
|
columns=["state"],
|
|
metrics=["SUM(num)", "MAX(num)"],
|
|
aggfunc="Sum as Fraction of Columns",
|
|
transpose_pivot=False,
|
|
combine_metrics=False,
|
|
show_rows_total=False,
|
|
show_columns_total=True,
|
|
apply_metrics_on_rows=False,
|
|
)
|
|
assert (
|
|
pivoted.to_markdown()
|
|
== """
|
|
| | ('SUM(num)', 'NULL') | ('MAX(num)', 'NULL') |
|
|
|:-------------------------------------------|-----------------------:|-----------------------:|
|
|
| ('boy', 'Edward') | 0.246295 | 0.138541 |
|
|
| ('boy', 'Tony') | 0.0389738 | 0.0701419 |
|
|
| ('boy', 'Subtotal') | 0.285269 | 0.208683 |
|
|
| ('girl', 'Amy') | 0.364227 | 0.255748 |
|
|
| ('girl', 'Cindy') | 0.0930273 | 0.0879057 |
|
|
| ('girl', 'Dawn') | 0.0998378 | 0.134307 |
|
|
| ('girl', 'Sophia') | 0.157639 | 0.313356 |
|
|
| ('girl', 'Subtotal') | 0.714731 | 0.791317 |
|
|
| ('Total (Sum as Fraction of Columns)', '') | 1 | 1 |
|
|
""".strip()
|
|
)
|
|
|
|
|
|
def test_table():
|
|
"""
|
|
Test that the table reports honor `d3NumberFormat`.
|
|
"""
|
|
df = pd.DataFrame.from_dict({"count": {0: 80679663}})
|
|
form_data = {
|
|
"adhoc_filters": [
|
|
{
|
|
"clause": "WHERE",
|
|
"comparator": "NULL",
|
|
"expressionType": "SIMPLE",
|
|
"filterOptionName": "filter_ameaka2efjv_rfv1et5nwng",
|
|
"isExtra": False,
|
|
"isNew": False,
|
|
"operator": "!=",
|
|
"sqlExpression": None,
|
|
"subject": "lang_at_home",
|
|
}
|
|
],
|
|
"all_columns": [],
|
|
"color_pn": True,
|
|
"column_config": {"count": {"d3NumberFormat": ",d"}},
|
|
"conditional_formatting": [],
|
|
"datasource": "8__table",
|
|
"extra_form_data": {},
|
|
"granularity_sqla": "time_start",
|
|
"groupby": ["lang_at_home"],
|
|
"metrics": ["count"],
|
|
"order_by_cols": [],
|
|
"order_desc": True,
|
|
"percent_metrics": [],
|
|
"query_mode": "aggregate",
|
|
"row_limit": "15",
|
|
"server_page_length": 10,
|
|
"show_cell_bars": True,
|
|
"table_timestamp_format": "smart_date",
|
|
"time_grain_sqla": "P1D",
|
|
"time_range": "No filter",
|
|
"url_params": {},
|
|
"viz_type": "table",
|
|
}
|
|
formatted = table(df, form_data)
|
|
assert (
|
|
formatted.to_markdown()
|
|
== """
|
|
| | count |
|
|
|---:|:-----------|
|
|
| 0 | 80,679,663 |
|
|
""".strip()
|
|
)
|
|
|
|
|
|
def test_apply_post_process_no_form_invalid_viz_type():
|
|
"""
|
|
Test with invalid viz type. It should just return the result
|
|
"""
|
|
result = {"foo": "bar"}
|
|
form_data = {"viz_type": "baz"}
|
|
assert apply_post_process(result, form_data) == result
|
|
|
|
|
|
def test_apply_post_process_without_result_format():
|
|
"""
|
|
A query without result_format should raise an exception
|
|
"""
|
|
result = {"queries": [{"result_format": "foo"}]}
|
|
form_data = {"viz_type": "pivot_table_v2"}
|
|
|
|
with pytest.raises(Exception) as ex:
|
|
apply_post_process(result, form_data)
|
|
|
|
assert ex.match("Result format foo not supported") == True
|
|
|
|
|
|
def test_apply_post_process_json_format():
|
|
"""
|
|
It should be able to process json results
|
|
"""
|
|
|
|
result = {
|
|
"queries": [
|
|
{
|
|
"result_format": ChartDataResultFormat.JSON,
|
|
"data": {
|
|
"result": [
|
|
{
|
|
"data": [{"COUNT(is_software_dev)": 4725}],
|
|
"colnames": ["COUNT(is_software_dev)"],
|
|
"coltypes": [0],
|
|
}
|
|
]
|
|
},
|
|
}
|
|
]
|
|
}
|
|
form_data = {
|
|
"datasource": "19__table",
|
|
"viz_type": "pivot_table_v2",
|
|
"slice_id": 69,
|
|
"url_params": {},
|
|
"granularity_sqla": "time_start",
|
|
"time_grain_sqla": "P1D",
|
|
"time_range": "No filter",
|
|
"groupbyColumns": [],
|
|
"groupbyRows": [],
|
|
"metrics": [
|
|
{
|
|
"aggregate": "COUNT",
|
|
"column": {
|
|
"column_name": "is_software_dev",
|
|
"description": None,
|
|
"expression": None,
|
|
"filterable": True,
|
|
"groupby": True,
|
|
"id": 1463,
|
|
"is_dttm": False,
|
|
"python_date_format": None,
|
|
"type": "DOUBLE PRECISION",
|
|
"verbose_name": None,
|
|
},
|
|
"expressionType": "SIMPLE",
|
|
"hasCustomLabel": False,
|
|
"isNew": False,
|
|
"label": "COUNT(is_software_dev)",
|
|
"optionName": "metric_9i1kctig9yr_sizo6ihd2o",
|
|
"sqlExpression": None,
|
|
}
|
|
],
|
|
"metricsLayout": "COLUMNS",
|
|
"adhoc_filters": [
|
|
{
|
|
"clause": "WHERE",
|
|
"comparator": "Currently A Developer",
|
|
"expressionType": "SIMPLE",
|
|
"filterOptionName": "filter_fvi0jg9aii_2lekqrhy7qk",
|
|
"isExtra": False,
|
|
"isNew": False,
|
|
"operator": "==",
|
|
"sqlExpression": None,
|
|
"subject": "developer_type",
|
|
}
|
|
],
|
|
"row_limit": 10000,
|
|
"order_desc": True,
|
|
"aggregateFunction": "Sum",
|
|
"valueFormat": "SMART_NUMBER",
|
|
"date_format": "smart_date",
|
|
"rowOrder": "key_a_to_z",
|
|
"colOrder": "key_a_to_z",
|
|
"extra_form_data": {},
|
|
"force": False,
|
|
"result_format": "json",
|
|
"result_type": "results",
|
|
}
|
|
|
|
assert apply_post_process(result, form_data) == {
|
|
"queries": [
|
|
{
|
|
"result_format": ChartDataResultFormat.JSON,
|
|
"data": {
|
|
"result": {
|
|
"Total (Sum)": {
|
|
"data": [{"COUNT(is_software_dev)": 4725}],
|
|
"colnames": ["COUNT(is_software_dev)"],
|
|
"coltypes": [0],
|
|
}
|
|
}
|
|
},
|
|
"colnames": [("result",)],
|
|
"indexnames": [("Total (Sum)",)],
|
|
"coltypes": [GenericDataType.STRING],
|
|
"rowcount": 1,
|
|
}
|
|
]
|
|
}
|
|
|
|
|
|
def test_apply_post_process_csv_format():
|
|
"""
|
|
It should be able to process csv results
|
|
"""
|
|
|
|
result = {
|
|
"queries": [
|
|
{
|
|
"result_format": ChartDataResultFormat.CSV,
|
|
"data": """
|
|
COUNT(is_software_dev)
|
|
4725
|
|
""",
|
|
}
|
|
]
|
|
}
|
|
form_data = {
|
|
"datasource": "19__table",
|
|
"viz_type": "pivot_table_v2",
|
|
"slice_id": 69,
|
|
"url_params": {},
|
|
"granularity_sqla": "time_start",
|
|
"time_grain_sqla": "P1D",
|
|
"time_range": "No filter",
|
|
"groupbyColumns": [],
|
|
"groupbyRows": [],
|
|
"metrics": [
|
|
{
|
|
"aggregate": "COUNT",
|
|
"column": {
|
|
"column_name": "is_software_dev",
|
|
"description": None,
|
|
"expression": None,
|
|
"filterable": True,
|
|
"groupby": True,
|
|
"id": 1463,
|
|
"is_dttm": False,
|
|
"python_date_format": None,
|
|
"type": "DOUBLE PRECISION",
|
|
"verbose_name": None,
|
|
},
|
|
"expressionType": "SIMPLE",
|
|
"hasCustomLabel": False,
|
|
"isNew": False,
|
|
"label": "COUNT(is_software_dev)",
|
|
"optionName": "metric_9i1kctig9yr_sizo6ihd2o",
|
|
"sqlExpression": None,
|
|
}
|
|
],
|
|
"metricsLayout": "COLUMNS",
|
|
"adhoc_filters": [
|
|
{
|
|
"clause": "WHERE",
|
|
"comparator": "Currently A Developer",
|
|
"expressionType": "SIMPLE",
|
|
"filterOptionName": "filter_fvi0jg9aii_2lekqrhy7qk",
|
|
"isExtra": False,
|
|
"isNew": False,
|
|
"operator": "==",
|
|
"sqlExpression": None,
|
|
"subject": "developer_type",
|
|
}
|
|
],
|
|
"row_limit": 10000,
|
|
"order_desc": True,
|
|
"aggregateFunction": "Sum",
|
|
"valueFormat": "SMART_NUMBER",
|
|
"date_format": "smart_date",
|
|
"rowOrder": "key_a_to_z",
|
|
"colOrder": "key_a_to_z",
|
|
"extra_form_data": {},
|
|
"force": False,
|
|
"result_format": "json",
|
|
"result_type": "results",
|
|
}
|
|
|
|
assert apply_post_process(result, form_data) == {
|
|
"queries": [
|
|
{
|
|
"result_format": ChartDataResultFormat.CSV,
|
|
"data": ",COUNT(is_software_dev)\nTotal (Sum),4725\n",
|
|
"colnames": [("COUNT(is_software_dev)",)],
|
|
"indexnames": [("Total (Sum)",)],
|
|
"coltypes": [GenericDataType.NUMERIC],
|
|
"rowcount": 1,
|
|
}
|
|
]
|
|
}
|
|
|
|
|
|
def test_apply_post_process_csv_format_empty_string():
|
|
"""
|
|
It should be able to process csv results with no data
|
|
"""
|
|
|
|
result = {"queries": [{"result_format": ChartDataResultFormat.CSV, "data": ""}]}
|
|
form_data = {
|
|
"datasource": "19__table",
|
|
"viz_type": "pivot_table_v2",
|
|
"slice_id": 69,
|
|
"url_params": {},
|
|
"granularity_sqla": "time_start",
|
|
"time_grain_sqla": "P1D",
|
|
"time_range": "No filter",
|
|
"groupbyColumns": [],
|
|
"groupbyRows": [],
|
|
"metrics": [
|
|
{
|
|
"aggregate": "COUNT",
|
|
"column": {
|
|
"column_name": "is_software_dev",
|
|
"description": None,
|
|
"expression": None,
|
|
"filterable": True,
|
|
"groupby": True,
|
|
"id": 1463,
|
|
"is_dttm": False,
|
|
"python_date_format": None,
|
|
"type": "DOUBLE PRECISION",
|
|
"verbose_name": None,
|
|
},
|
|
"expressionType": "SIMPLE",
|
|
"hasCustomLabel": False,
|
|
"isNew": False,
|
|
"label": "COUNT(is_software_dev)",
|
|
"optionName": "metric_9i1kctig9yr_sizo6ihd2o",
|
|
"sqlExpression": None,
|
|
}
|
|
],
|
|
"metricsLayout": "COLUMNS",
|
|
"adhoc_filters": [
|
|
{
|
|
"clause": "WHERE",
|
|
"comparator": "Currently A Developer",
|
|
"expressionType": "SIMPLE",
|
|
"filterOptionName": "filter_fvi0jg9aii_2lekqrhy7qk",
|
|
"isExtra": False,
|
|
"isNew": False,
|
|
"operator": "==",
|
|
"sqlExpression": None,
|
|
"subject": "developer_type",
|
|
}
|
|
],
|
|
"row_limit": 10000,
|
|
"order_desc": True,
|
|
"aggregateFunction": "Sum",
|
|
"valueFormat": "SMART_NUMBER",
|
|
"date_format": "smart_date",
|
|
"rowOrder": "key_a_to_z",
|
|
"colOrder": "key_a_to_z",
|
|
"extra_form_data": {},
|
|
"force": False,
|
|
"result_format": "json",
|
|
"result_type": "results",
|
|
}
|
|
|
|
assert apply_post_process(result, form_data) == {
|
|
"queries": [{"result_format": ChartDataResultFormat.CSV, "data": ""}]
|
|
}
|
|
|
|
|
|
@pytest.mark.parametrize("data", [None, "", "\n"])
|
|
def test_apply_post_process_csv_format_no_data(data):
|
|
"""
|
|
It should be able to process csv results with no data
|
|
"""
|
|
|
|
result = {"queries": [{"result_format": ChartDataResultFormat.CSV, "data": data}]}
|
|
form_data = {
|
|
"datasource": "19__table",
|
|
"viz_type": "pivot_table_v2",
|
|
"slice_id": 69,
|
|
"url_params": {},
|
|
"granularity_sqla": "time_start",
|
|
"time_grain_sqla": "P1D",
|
|
"time_range": "No filter",
|
|
"groupbyColumns": [],
|
|
"groupbyRows": [],
|
|
"metrics": [
|
|
{
|
|
"aggregate": "COUNT",
|
|
"column": {
|
|
"column_name": "is_software_dev",
|
|
"description": None,
|
|
"expression": None,
|
|
"filterable": True,
|
|
"groupby": True,
|
|
"id": 1463,
|
|
"is_dttm": False,
|
|
"python_date_format": None,
|
|
"type": "DOUBLE PRECISION",
|
|
"verbose_name": None,
|
|
},
|
|
"expressionType": "SIMPLE",
|
|
"hasCustomLabel": False,
|
|
"isNew": False,
|
|
"label": "COUNT(is_software_dev)",
|
|
"optionName": "metric_9i1kctig9yr_sizo6ihd2o",
|
|
"sqlExpression": None,
|
|
}
|
|
],
|
|
"metricsLayout": "COLUMNS",
|
|
"adhoc_filters": [
|
|
{
|
|
"clause": "WHERE",
|
|
"comparator": "Currently A Developer",
|
|
"expressionType": "SIMPLE",
|
|
"filterOptionName": "filter_fvi0jg9aii_2lekqrhy7qk",
|
|
"isExtra": False,
|
|
"isNew": False,
|
|
"operator": "==",
|
|
"sqlExpression": None,
|
|
"subject": "developer_type",
|
|
}
|
|
],
|
|
"row_limit": 10000,
|
|
"order_desc": True,
|
|
"aggregateFunction": "Sum",
|
|
"valueFormat": "SMART_NUMBER",
|
|
"date_format": "smart_date",
|
|
"rowOrder": "key_a_to_z",
|
|
"colOrder": "key_a_to_z",
|
|
"extra_form_data": {},
|
|
"force": False,
|
|
"result_format": "json",
|
|
"result_type": "results",
|
|
}
|
|
|
|
assert apply_post_process(result, form_data) == {
|
|
"queries": [{"result_format": ChartDataResultFormat.CSV, "data": data}]
|
|
}
|
|
|
|
|
|
def test_apply_post_process_csv_format_no_data_multiple_queries():
|
|
"""
|
|
It should be able to process csv results multiple queries if one query has no data
|
|
"""
|
|
|
|
result = {
|
|
"queries": [
|
|
{"result_format": ChartDataResultFormat.CSV, "data": ""},
|
|
{
|
|
"result_format": ChartDataResultFormat.CSV,
|
|
"data": """
|
|
COUNT(is_software_dev)
|
|
4725
|
|
""",
|
|
},
|
|
]
|
|
}
|
|
form_data = {
|
|
"datasource": "19__table",
|
|
"viz_type": "pivot_table_v2",
|
|
"slice_id": 69,
|
|
"url_params": {},
|
|
"granularity_sqla": "time_start",
|
|
"time_grain_sqla": "P1D",
|
|
"time_range": "No filter",
|
|
"groupbyColumns": [],
|
|
"groupbyRows": [],
|
|
"metrics": [
|
|
{
|
|
"aggregate": "COUNT",
|
|
"column": {
|
|
"column_name": "is_software_dev",
|
|
"description": None,
|
|
"expression": None,
|
|
"filterable": True,
|
|
"groupby": True,
|
|
"id": 1463,
|
|
"is_dttm": False,
|
|
"python_date_format": None,
|
|
"type": "DOUBLE PRECISION",
|
|
"verbose_name": None,
|
|
},
|
|
"expressionType": "SIMPLE",
|
|
"hasCustomLabel": False,
|
|
"isNew": False,
|
|
"label": "COUNT(is_software_dev)",
|
|
"optionName": "metric_9i1kctig9yr_sizo6ihd2o",
|
|
"sqlExpression": None,
|
|
}
|
|
],
|
|
"metricsLayout": "COLUMNS",
|
|
"adhoc_filters": [
|
|
{
|
|
"clause": "WHERE",
|
|
"comparator": "Currently A Developer",
|
|
"expressionType": "SIMPLE",
|
|
"filterOptionName": "filter_fvi0jg9aii_2lekqrhy7qk",
|
|
"isExtra": False,
|
|
"isNew": False,
|
|
"operator": "==",
|
|
"sqlExpression": None,
|
|
"subject": "developer_type",
|
|
}
|
|
],
|
|
"row_limit": 10000,
|
|
"order_desc": True,
|
|
"aggregateFunction": "Sum",
|
|
"valueFormat": "SMART_NUMBER",
|
|
"date_format": "smart_date",
|
|
"rowOrder": "key_a_to_z",
|
|
"colOrder": "key_a_to_z",
|
|
"extra_form_data": {},
|
|
"force": False,
|
|
"result_format": "json",
|
|
"result_type": "results",
|
|
}
|
|
|
|
assert apply_post_process(result, form_data) == {
|
|
"queries": [
|
|
{"result_format": ChartDataResultFormat.CSV, "data": ""},
|
|
{
|
|
"result_format": ChartDataResultFormat.CSV,
|
|
"data": ",COUNT(is_software_dev)\nTotal (Sum),4725\n",
|
|
"colnames": [("COUNT(is_software_dev)",)],
|
|
"indexnames": [("Total (Sum)",)],
|
|
"coltypes": [GenericDataType.NUMERIC],
|
|
"rowcount": 1,
|
|
},
|
|
]
|
|
}
|
|
|
|
|
|
def test_apply_post_process_json_format_empty_string():
|
|
"""
|
|
It should be able to process json results with no data
|
|
"""
|
|
|
|
result = {"queries": [{"result_format": ChartDataResultFormat.JSON, "data": ""}]}
|
|
form_data = {
|
|
"datasource": "19__table",
|
|
"viz_type": "pivot_table_v2",
|
|
"slice_id": 69,
|
|
"url_params": {},
|
|
"granularity_sqla": "time_start",
|
|
"time_grain_sqla": "P1D",
|
|
"time_range": "No filter",
|
|
"groupbyColumns": [],
|
|
"groupbyRows": [],
|
|
"metrics": [
|
|
{
|
|
"aggregate": "COUNT",
|
|
"column": {
|
|
"column_name": "is_software_dev",
|
|
"description": None,
|
|
"expression": None,
|
|
"filterable": True,
|
|
"groupby": True,
|
|
"id": 1463,
|
|
"is_dttm": False,
|
|
"python_date_format": None,
|
|
"type": "DOUBLE PRECISION",
|
|
"verbose_name": None,
|
|
},
|
|
"expressionType": "SIMPLE",
|
|
"hasCustomLabel": False,
|
|
"isNew": False,
|
|
"label": "COUNT(is_software_dev)",
|
|
"optionName": "metric_9i1kctig9yr_sizo6ihd2o",
|
|
"sqlExpression": None,
|
|
}
|
|
],
|
|
"metricsLayout": "COLUMNS",
|
|
"adhoc_filters": [
|
|
{
|
|
"clause": "WHERE",
|
|
"comparator": "Currently A Developer",
|
|
"expressionType": "SIMPLE",
|
|
"filterOptionName": "filter_fvi0jg9aii_2lekqrhy7qk",
|
|
"isExtra": False,
|
|
"isNew": False,
|
|
"operator": "==",
|
|
"sqlExpression": None,
|
|
"subject": "developer_type",
|
|
}
|
|
],
|
|
"row_limit": 10000,
|
|
"order_desc": True,
|
|
"aggregateFunction": "Sum",
|
|
"valueFormat": "SMART_NUMBER",
|
|
"date_format": "smart_date",
|
|
"rowOrder": "key_a_to_z",
|
|
"colOrder": "key_a_to_z",
|
|
"extra_form_data": {},
|
|
"force": False,
|
|
"result_format": "json",
|
|
"result_type": "results",
|
|
}
|
|
|
|
assert apply_post_process(result, form_data) == {
|
|
"queries": [{"result_format": ChartDataResultFormat.JSON, "data": ""}]
|
|
}
|
|
|
|
|
|
def test_apply_post_process_json_format_data_is_none():
|
|
"""
|
|
It should be able to process json results with no data
|
|
"""
|
|
|
|
result = {"queries": [{"result_format": ChartDataResultFormat.JSON, "data": None}]}
|
|
form_data = {
|
|
"datasource": "19__table",
|
|
"viz_type": "pivot_table_v2",
|
|
"slice_id": 69,
|
|
"url_params": {},
|
|
"granularity_sqla": "time_start",
|
|
"time_grain_sqla": "P1D",
|
|
"time_range": "No filter",
|
|
"groupbyColumns": [],
|
|
"groupbyRows": [],
|
|
"metrics": [
|
|
{
|
|
"aggregate": "COUNT",
|
|
"column": {
|
|
"column_name": "is_software_dev",
|
|
"description": None,
|
|
"expression": None,
|
|
"filterable": True,
|
|
"groupby": True,
|
|
"id": 1463,
|
|
"is_dttm": False,
|
|
"python_date_format": None,
|
|
"type": "DOUBLE PRECISION",
|
|
"verbose_name": None,
|
|
},
|
|
"expressionType": "SIMPLE",
|
|
"hasCustomLabel": False,
|
|
"isNew": False,
|
|
"label": "COUNT(is_software_dev)",
|
|
"optionName": "metric_9i1kctig9yr_sizo6ihd2o",
|
|
"sqlExpression": None,
|
|
}
|
|
],
|
|
"metricsLayout": "COLUMNS",
|
|
"adhoc_filters": [
|
|
{
|
|
"clause": "WHERE",
|
|
"comparator": "Currently A Developer",
|
|
"expressionType": "SIMPLE",
|
|
"filterOptionName": "filter_fvi0jg9aii_2lekqrhy7qk",
|
|
"isExtra": False,
|
|
"isNew": False,
|
|
"operator": "==",
|
|
"sqlExpression": None,
|
|
"subject": "developer_type",
|
|
}
|
|
],
|
|
"row_limit": 10000,
|
|
"order_desc": True,
|
|
"aggregateFunction": "Sum",
|
|
"valueFormat": "SMART_NUMBER",
|
|
"date_format": "smart_date",
|
|
"rowOrder": "key_a_to_z",
|
|
"colOrder": "key_a_to_z",
|
|
"extra_form_data": {},
|
|
"force": False,
|
|
"result_format": "json",
|
|
"result_type": "results",
|
|
}
|
|
|
|
assert apply_post_process(result, form_data) == {
|
|
"queries": [{"result_format": ChartDataResultFormat.JSON, "data": None}]
|
|
}
|
|
|
|
|
|
def test_apply_post_process_verbose_map(session: Session):
|
|
from superset import db
|
|
from superset.connectors.sqla.models import SqlaTable, SqlMetric
|
|
from superset.models.core import Database
|
|
|
|
engine = db.session.get_bind()
|
|
SqlaTable.metadata.create_all(engine) # pylint: disable=no-member
|
|
database = Database(database_name="my_database", sqlalchemy_uri="sqlite://")
|
|
sqla_table = SqlaTable(
|
|
table_name="my_sqla_table",
|
|
columns=[],
|
|
metrics=[
|
|
SqlMetric(
|
|
metric_name="count",
|
|
verbose_name="COUNT(*)",
|
|
metric_type="count",
|
|
expression="COUNT(*)",
|
|
)
|
|
],
|
|
database=database,
|
|
)
|
|
|
|
result = {
|
|
"queries": [
|
|
{
|
|
"result_format": ChartDataResultFormat.JSON,
|
|
"data": [{"count": 4725}],
|
|
}
|
|
]
|
|
}
|
|
form_data = {
|
|
"datasource": "19__table",
|
|
"viz_type": "pivot_table_v2",
|
|
"slice_id": 69,
|
|
"url_params": {},
|
|
"granularity_sqla": "time_start",
|
|
"time_grain_sqla": "P1D",
|
|
"time_range": "No filter",
|
|
"groupbyColumns": [],
|
|
"groupbyRows": [],
|
|
"metrics": ["COUNT(*)"],
|
|
"metricsLayout": "COLUMNS",
|
|
"row_limit": 10000,
|
|
"order_desc": True,
|
|
"valueFormat": "SMART_NUMBER",
|
|
"date_format": "smart_date",
|
|
"rowOrder": "key_a_to_z",
|
|
"colOrder": "key_a_to_z",
|
|
"extra_form_data": {},
|
|
"force": False,
|
|
"result_format": "json",
|
|
"result_type": "results",
|
|
}
|
|
|
|
assert apply_post_process(result, form_data, datasource=sqla_table) == {
|
|
"queries": [
|
|
{
|
|
"result_format": ChartDataResultFormat.JSON,
|
|
"data": {"COUNT(*)": {"Total (Sum)": 4725}},
|
|
"colnames": [("COUNT(*)",)],
|
|
"indexnames": [("Total (Sum)",)],
|
|
"coltypes": [GenericDataType.NUMERIC],
|
|
"rowcount": 1,
|
|
}
|
|
]
|
|
}
|