# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. import copy from typing import Any, Dict import pandas as pd from superset.charts.post_processing import apply_post_process, pivot_df from superset.utils.core import GenericDataType, QueryStatus RESULT: Dict[str, Any] = { "query_context": None, "queries": [ { "cache_key": "1bd3ab8c01e98a0e349fb61bc76d9b90", "cached_dttm": None, "cache_timeout": 86400, "annotation_data": {}, "error": None, "is_cached": None, "query": """SELECT state AS state, gender AS gender, sum(num) AS \"Births\" FROM birth_names WHERE ds >= TO_TIMESTAMP('1921-07-28 00:00:00.000000', 'YYYY-MM-DD HH24:MI:SS.US') AND ds < TO_TIMESTAMP('2021-07-28 10:39:44.000000', 'YYYY-MM-DD HH24:MI:SS.US') GROUP BY state, gender LIMIT 50000; """, "status": QueryStatus.SUCCESS, "stacktrace": None, "rowcount": 22, "colnames": ["state", "gender", "Births"], "coltypes": [ GenericDataType.STRING, GenericDataType.STRING, GenericDataType.NUMERIC, ], "data": """state,gender,Births OH,boy,2376385 TX,girl,2313186 MA,boy,1285126 MA,girl,842146 PA,boy,2390275 NY,boy,3543961 FL,boy,1968060 TX,boy,3311985 NJ,boy,1486126 CA,girl,3567754 CA,boy,5430796 IL,girl,1614427 FL,girl,1312593 NY,girl,2280733 NJ,girl,992702 MI,girl,1326229 other,girl,15058341 other,boy,22044909 MI,boy,1938321 IL,boy,2357411 PA,girl,1615383 OH,girl,1622814 """, "applied_filters": [], "rejected_filters": [], } ], } def test_pivot_table(): form_data = { "adhoc_filters": [], "columns": ["state"], "datasource": "3__table", "date_format": "smart_date", "extra_form_data": {}, "granularity_sqla": "ds", "groupby": ["gender"], "metrics": [ { "aggregate": "SUM", "column": {"column_name": "num", "type": "BIGINT"}, "expressionType": "SIMPLE", "label": "Births", "optionName": "metric_11", } ], "number_format": "SMART_NUMBER", "order_desc": True, "pandas_aggfunc": "sum", "pivot_margins": True, "row_limit": 50000, "slice_id": 143, "time_grain_sqla": "P1D", "time_range": "100 years ago : now", "time_range_endpoints": ["inclusive", "exclusive"], "url_params": {}, "viz_type": "pivot_table", } result = copy.deepcopy(RESULT) assert apply_post_process(result, form_data) == { "query_context": None, "queries": [ { "cache_key": "1bd3ab8c01e98a0e349fb61bc76d9b90", "cached_dttm": None, "cache_timeout": 86400, "annotation_data": {}, "error": None, "is_cached": None, "query": """SELECT state AS state, gender AS gender, sum(num) AS \"Births\" FROM birth_names WHERE ds >= TO_TIMESTAMP('1921-07-28 00:00:00.000000', 'YYYY-MM-DD HH24:MI:SS.US') AND ds < TO_TIMESTAMP('2021-07-28 10:39:44.000000', 'YYYY-MM-DD HH24:MI:SS.US') GROUP BY state, gender LIMIT 50000; """, "status": QueryStatus.SUCCESS, "stacktrace": None, "rowcount": 3, "colnames": [ "Births CA", "Births FL", "Births IL", "Births MA", "Births MI", "Births NJ", "Births NY", "Births OH", "Births PA", "Births TX", "Births other", "Births Subtotal", "Total (Sum)", ], "coltypes": [ GenericDataType.NUMERIC, GenericDataType.NUMERIC, GenericDataType.NUMERIC, GenericDataType.NUMERIC, GenericDataType.NUMERIC, GenericDataType.NUMERIC, GenericDataType.NUMERIC, GenericDataType.NUMERIC, GenericDataType.NUMERIC, GenericDataType.NUMERIC, GenericDataType.NUMERIC, GenericDataType.NUMERIC, GenericDataType.NUMERIC, ], "data": """,Births CA,Births FL,Births IL,Births MA,Births MI,Births NJ,Births NY,Births OH,Births PA,Births TX,Births other,Births Subtotal,Total (Sum) boy,5430796,1968060,2357411,1285126,1938321,1486126,3543961,2376385,2390275,3311985,22044909,48133355,48133355 girl,3567754,1312593,1614427,842146,1326229,992702,2280733,1622814,1615383,2313186,15058341,32546308,32546308 Total (Sum),8998550,3280653,3971838,2127272,3264550,2478828,5824694,3999199,4005658,5625171,37103250,80679663,80679663 """, "applied_filters": [], "rejected_filters": [], } ], } def test_pivot_table_v2(): form_data = { "adhoc_filters": [], "aggregateFunction": "Sum as Fraction of Rows", "colOrder": "key_a_to_z", "colTotals": True, "combineMetric": True, "datasource": "3__table", "date_format": "smart_date", "extra_form_data": {}, "granularity_sqla": "ds", "groupbyColumns": ["state"], "groupbyRows": ["gender"], "metrics": [ { "aggregate": "SUM", "column": {"column_name": "num", "type": "BIGINT"}, "expressionType": "SIMPLE", "label": "Births", "optionName": "metric_11", } ], "metricsLayout": "COLUMNS", "rowOrder": "key_a_to_z", "rowTotals": True, "row_limit": 50000, "slice_id": 72, "time_grain_sqla": None, "time_range": "100 years ago : now", "time_range_endpoints": ["inclusive", "exclusive"], "transposePivot": True, "url_params": {}, "valueFormat": "SMART_NUMBER", "viz_type": "pivot_table_v2", } result = copy.deepcopy(RESULT) assert apply_post_process(result, form_data) == { "query_context": None, "queries": [ { "cache_key": "1bd3ab8c01e98a0e349fb61bc76d9b90", "cached_dttm": None, "cache_timeout": 86400, "annotation_data": {}, "error": None, "is_cached": None, "query": """SELECT state AS state, gender AS gender, sum(num) AS \"Births\" FROM birth_names WHERE ds >= TO_TIMESTAMP('1921-07-28 00:00:00.000000', 'YYYY-MM-DD HH24:MI:SS.US') AND ds < TO_TIMESTAMP('2021-07-28 10:39:44.000000', 'YYYY-MM-DD HH24:MI:SS.US') GROUP BY state, gender LIMIT 50000; """, "status": QueryStatus.SUCCESS, "stacktrace": None, "rowcount": 12, "colnames": [ "boy Births", "boy Subtotal", "girl Births", "girl Subtotal", "Total (Sum as Fraction of Rows)", ], "coltypes": [ GenericDataType.NUMERIC, GenericDataType.NUMERIC, GenericDataType.NUMERIC, GenericDataType.NUMERIC, GenericDataType.NUMERIC, ], "data": """,boy Births,boy Subtotal,girl Births,girl Subtotal,Total (Sum as Fraction of Rows) CA,0.6035190113962805,0.6035190113962805,0.3964809886037195,0.3964809886037195,1.0 FL,0.5998988615985903,0.5998988615985903,0.4001011384014097,0.4001011384014097,1.0 IL,0.5935315085862012,0.5935315085862012,0.40646849141379887,0.40646849141379887,1.0 MA,0.6041192663655611,0.6041192663655611,0.3958807336344389,0.3958807336344389,1.0 MI,0.5937482960898133,0.5937482960898133,0.4062517039101867,0.4062517039101867,1.0 NJ,0.5995276800165239,0.5995276800165239,0.40047231998347604,0.40047231998347604,1.0 NY,0.6084372844307357,0.6084372844307357,0.39156271556926425,0.39156271556926425,1.0 OH,0.5942152416021308,0.5942152416021308,0.40578475839786915,0.40578475839786915,1.0 PA,0.596724682935987,0.596724682935987,0.40327531706401293,0.40327531706401293,1.0 TX,0.5887794344385264,0.5887794344385264,0.41122056556147357,0.41122056556147357,1.0 other,0.5941503507105172,0.5941503507105172,0.40584964928948275,0.40584964928948275,1.0 Total (Sum as Fraction of Rows),6.576651618170867,6.576651618170867,4.423348381829133,4.423348381829133,11.0 """, "applied_filters": [], "rejected_filters": [], } ], } def test_pivot_df_no_cols_no_rows_single_metric(): """ Pivot table when no cols/rows and 1 metric are selected. """ # when no cols/rows are selected there are no groupbys in the query, # and the data has only the metric(s) df = pd.DataFrame.from_dict({"SUM(num)": {0: 80679663}}) assert ( df.to_markdown() == """ | | SUM(num) | |---:|------------:| | 0 | 8.06797e+07 | """.strip() ) pivoted = pivot_df( df, rows=[], columns=[], metrics=["SUM(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() == """ | metric | SUM(num) | |:------------|------------:| | Total (Sum) | 8.06797e+07 | """.strip() ) # tranpose_pivot and combine_metrics do nothing in this case pivoted = pivot_df( df, rows=[], columns=[], metrics=["SUM(num)"], aggfunc="Sum", transpose_pivot=True, combine_metrics=True, show_rows_total=False, show_columns_total=False, apply_metrics_on_rows=False, ) assert ( pivoted.to_markdown() == """ | metric | SUM(num) | |:------------|------------:| | Total (Sum) | 8.06797e+07 | """.strip() ) # apply_metrics_on_rows will pivot the table, moving the metrics # to rows pivoted = pivot_df( df, rows=[], columns=[], metrics=["SUM(num)"], aggfunc="Sum", transpose_pivot=True, combine_metrics=True, show_rows_total=False, show_columns_total=False, apply_metrics_on_rows=True, ) assert ( pivoted.to_markdown() == """ | | Total (Sum) | |:---------|--------------:| | SUM(num) | 8.06797e+07 | """.strip() ) # showing totals pivoted = pivot_df( df, rows=[], columns=[], metrics=["SUM(num)"], aggfunc="Sum", transpose_pivot=True, combine_metrics=True, show_rows_total=True, show_columns_total=True, apply_metrics_on_rows=False, ) assert ( pivoted.to_markdown() == """ | metric | ('SUM(num)',) | ('Total (Sum)',) | |:------------|----------------:|-------------------:| | Total (Sum) | 8.06797e+07 | 8.06797e+07 | """.strip() ) def test_pivot_df_no_cols_no_rows_two_metrics(): """ Pivot table when no cols/rows and 2 metrics are selected. """ # when no cols/rows are selected there are no groupbys in the query, # and the data has only the metrics df = pd.DataFrame.from_dict({"SUM(num)": {0: 80679663}, "MAX(num)": {0: 37296}}) assert ( df.to_markdown() == """ | | SUM(num) | MAX(num) | |---:|------------:|-----------:| | 0 | 8.06797e+07 | 37296 | """.strip() ) pivoted = pivot_df( df, rows=[], columns=[], 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() == """ | metric | SUM(num) | MAX(num) | |:------------|------------:|-----------:| | Total (Sum) | 8.06797e+07 | 37296 | """.strip() ) # tranpose_pivot and combine_metrics do nothing in this case pivoted = pivot_df( df, rows=[], columns=[], metrics=["SUM(num)", "MAX(num)"], aggfunc="Sum", transpose_pivot=True, combine_metrics=True, show_rows_total=False, show_columns_total=False, apply_metrics_on_rows=False, ) assert ( pivoted.to_markdown() == """ | metric | SUM(num) | MAX(num) | |:------------|------------:|-----------:| | Total (Sum) | 8.06797e+07 | 37296 | """.strip() ) # apply_metrics_on_rows will pivot the table, moving the metrics # to rows pivoted = pivot_df( df, rows=[], columns=[], metrics=["SUM(num)", "MAX(num)"], aggfunc="Sum", transpose_pivot=True, combine_metrics=True, show_rows_total=False, show_columns_total=False, apply_metrics_on_rows=True, ) assert ( pivoted.to_markdown() == """ | | Total (Sum) | |:---------|----------------:| | SUM(num) | 8.06797e+07 | | MAX(num) | 37296 | """.strip() ) # when showing totals we only add a column, since adding a row # would be redundant pivoted = pivot_df( df, rows=[], columns=[], 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=False, ) assert ( pivoted.to_markdown() == """ | metric | ('SUM(num)',) | ('MAX(num)',) | ('Total (Sum)',) | |:------------|----------------:|----------------:|-------------------:| | Total (Sum) | 8.06797e+07 | 37296 | 8.0717e+07 | """.strip() ) def test_pivot_df_single_row_two_metrics(): """ Pivot table when a single column and 2 metrics are selected. """ df = pd.DataFrame.from_dict( { "gender": {0: "girl", 1: "boy"}, "SUM(num)": {0: 118065, 1: 47123}, "MAX(num)": {0: 2588, 1: 1280}, } ) assert ( df.to_markdown() == """ | | gender | SUM(num) | MAX(num) | |---:|:---------|-----------:|-----------:| | 0 | girl | 118065 | 2588 | | 1 | boy | 47123 | 1280 | """.strip() ) pivoted = pivot_df( df, rows=["gender"], columns=[], 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() == """ | gender | SUM(num) | MAX(num) | |:---------|-----------:|-----------:| | boy | 47123 | 1280 | | girl | 118065 | 2588 | """.strip() ) # transpose_pivot pivoted = pivot_df( df, rows=["gender"], columns=[], 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() == """ | metric | ('SUM(num)', 'boy') | ('SUM(num)', 'girl') | ('MAX(num)', 'boy') | ('MAX(num)', 'girl') | |:------------|----------------------:|-----------------------:|----------------------:|-----------------------:| | Total (Sum) | 47123 | 118065 | 1280 | 2588 | """.strip() ) # combine_metrics does nothing in this case pivoted = pivot_df( df, rows=["gender"], columns=[], 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() == """ | gender | SUM(num) | MAX(num) | |:---------|-----------:|-----------:| | boy | 47123 | 1280 | | girl | 118065 | 2588 | """.strip() ) # show totals pivoted = pivot_df( df, rows=["gender"], columns=[], 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)',) | ('MAX(num)',) | ('Total (Sum)',) | |:-----------------|----------------:|----------------:|-------------------:| | ('boy',) | 47123 | 1280 | 48403 | | ('girl',) | 118065 | 2588 | 120653 | | ('Total (Sum)',) | 165188 | 3868 | 169056 | """.strip() ) # apply_metrics_on_rows pivoted = pivot_df( df, rows=["gender"], columns=[], 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=True, ) assert ( pivoted.to_markdown() == """ | | Total (Sum) | |:-------------------------|--------------:| | ('SUM(num)', 'boy') | 47123 | | ('SUM(num)', 'girl') | 118065 | | ('SUM(num)', 'Subtotal') | 165188 | | ('MAX(num)', 'boy') | 1280 | | ('MAX(num)', 'girl') | 2588 | | ('MAX(num)', 'Subtotal') | 3868 | | ('Total (Sum)', '') | 169056 | """.strip() ) # apply_metrics_on_rows with combine_metrics pivoted = pivot_df( df, rows=["gender"], columns=[], metrics=["SUM(num)", "MAX(num)"], aggfunc="Sum", transpose_pivot=False, combine_metrics=True, show_rows_total=True, show_columns_total=True, apply_metrics_on_rows=True, ) assert ( pivoted.to_markdown() == """ | | Total (Sum) | |:---------------------|--------------:| | ('boy', 'SUM(num)') | 47123 | | ('boy', 'MAX(num)') | 1280 | | ('boy', 'Subtotal') | 48403 | | ('girl', 'SUM(num)') | 118065 | | ('girl', 'MAX(num)') | 2588 | | ('girl', 'Subtotal') | 120653 | | ('Total (Sum)', '') | 169056 | """.strip() ) def test_pivot_df_complex(): """ Pivot table when a column, 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", }, "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 | CA | girl | Amy | 45426 | 2227 | | 1 | CA | boy | Edward | 31290 | 1280 | | 2 | CA | girl | Sophia | 18859 | 2588 | | 3 | FL | girl | Amy | 14740 | 854 | | 4 | CA | girl | Cindy | 14149 | 842 | | 5 | CA | girl | Dawn | 11403 | 1157 | | 6 | FL | boy | Edward | 9395 | 389 | | 7 | FL | girl | Sophia | 7181 | 1187 | | 8 | FL | girl | Dawn | 5089 | 461 | | 9 | CA | boy | Tony | 3765 | 598 | | 10 | FL | boy | Tony | 2673 | 247 | | 11 | FL | 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)', 'CA') | ('SUM(num)', 'FL') | ('MAX(num)', 'CA') | ('MAX(num)', 'FL') | |:-------------------|---------------------:|---------------------:|---------------------:|---------------------:| | ('boy', 'Edward') | 31290 | 9395 | 1280 | 389 | | ('boy', 'Tony') | 3765 | 2673 | 598 | 247 | | ('girl', 'Amy') | 45426 | 14740 | 2227 | 854 | | ('girl', 'Cindy') | 14149 | 1218 | 842 | 217 | | ('girl', 'Dawn') | 11403 | 5089 | 1157 | 461 | | ('girl', 'Sophia') | 18859 | 7181 | 2588 | 1187 | """.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() == """ | state | ('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() )