mirror of https://github.com/apache/superset.git
fix: improve pivot post-processing (#16289)
* fix: improve pivot post-processing * Add tests * Trim space from column name
This commit is contained in:
Beto Dealmeida
GitHub
parent
3c0aefb61a
commit
ac8e54d909
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@ -27,60 +27,151 @@ for these chart types.
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"""
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from io import StringIO
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from typing import Any, Callable, Dict, Optional, Union
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from typing import Any, Dict, List, Optional, Tuple
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import pandas as pd
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from superset.utils.core import DTTM_ALIAS, extract_dataframe_dtypes, get_metric_name
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def sql_like_sum(series: pd.Series) -> pd.Series:
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def get_column_key(label: Tuple[str, ...], metrics: List[str]) -> Tuple[Any, ...]:
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"""
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A SUM aggregation function that mimics the behavior from SQL.
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Sort columns when combining metrics.
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MultiIndex labels have the metric name as the last element in the
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tuple. We want to sort these according to the list of passed metrics.
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"""
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return series.sum(min_count=1)
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parts: List[Any] = list(label)
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metric = parts[-1]
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parts[-1] = metrics.index(metric)
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return tuple(parts)
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def pivot_table(df: pd.DataFrame, form_data: Dict[str, Any]) -> pd.DataFrame:
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"""
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Pivot table.
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"""
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if form_data.get("granularity") == "all" and DTTM_ALIAS in df:
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del df[DTTM_ALIAS]
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def pivot_df( # pylint: disable=too-many-locals, too-many-arguments, too-many-statements, too-many-branches
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df: pd.DataFrame,
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rows: List[str],
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columns: List[str],
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metrics: List[str],
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aggfunc: str = "Sum",
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transpose_pivot: bool = False,
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combine_metrics: bool = False,
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show_rows_total: bool = False,
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show_columns_total: bool = False,
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apply_metrics_on_rows: bool = False,
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) -> pd.DataFrame:
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metric_name = f"Total ({aggfunc})"
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metrics = [get_metric_name(m) for m in form_data["metrics"]]
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aggfuncs: Dict[str, Union[str, Callable[[Any], Any]]] = {}
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for metric in metrics:
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aggfunc = form_data.get("pandas_aggfunc") or "sum"
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if pd.api.types.is_numeric_dtype(df[metric]):
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if aggfunc == "sum":
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aggfunc = sql_like_sum
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elif aggfunc not in {"min", "max"}:
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aggfunc = "max"
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aggfuncs[metric] = aggfunc
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if transpose_pivot:
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rows, columns = columns, rows
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groupby = form_data.get("groupby") or []
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columns = form_data.get("columns") or []
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if form_data.get("transpose_pivot"):
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groupby, columns = columns, groupby
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# to apply the metrics on the rows we pivot the dataframe, apply the
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# metrics to the columns, and pivot the dataframe back before
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# returning it
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if apply_metrics_on_rows:
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rows, columns = columns, rows
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axis = {"columns": 0, "rows": 1}
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else:
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axis = {"columns": 1, "rows": 0}
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df = df.pivot_table(
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index=groupby,
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columns=columns,
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values=metrics,
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aggfunc=aggfuncs,
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margins=form_data.get("pivot_margins"),
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)
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# pivot data; we'll compute totals and subtotals later
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if rows or columns:
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df = df.pivot_table(
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index=rows,
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columns=columns,
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values=metrics,
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aggfunc=pivot_v2_aggfunc_map[aggfunc],
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margins=False,
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)
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else:
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# if there's no rows nor columns we have a single value; update
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# the index with the metric name so it shows up in the table
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df.index = pd.Index([*df.index[:-1], metric_name], name="metric")
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# Display metrics side by side with each column
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if form_data.get("combine_metric"):
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df = df.stack(0).unstack().reindex(level=-1, columns=metrics)
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# if no rows were passed the metrics will be in the rows, so we
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# need to move them back to columns
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if columns and not rows:
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df = df.stack().to_frame().T
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df = df[metrics]
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df.index = pd.Index([*df.index[:-1], metric_name], name="metric")
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# flatten column names
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df.columns = [
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" ".join(str(name) for name in column) if isinstance(column, tuple) else column
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for column in df.columns
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]
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# combining metrics changes the column hierarchy, moving the metric
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# from the top to the bottom, eg:
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#
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# ('SUM(col)', 'age', 'name') => ('age', 'name', 'SUM(col)')
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if combine_metrics and isinstance(df.columns, pd.MultiIndex):
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# move metrics to the lowest level
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new_order = [*range(1, df.columns.nlevels), 0]
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df = df.reorder_levels(new_order, axis=1)
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# sort columns, combining metrics for each group
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decorated_columns = [(col, i) for i, col in enumerate(df.columns)]
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grouped_columns = sorted(
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decorated_columns, key=lambda t: get_column_key(t[0], metrics)
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)
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indexes = [i for col, i in grouped_columns]
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df = df[df.columns[indexes]]
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elif rows:
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# if metrics were not combined we sort the dataframe by the list
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# of metrics defined by the user
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df = df[metrics]
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# compute fractions, if needed
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if aggfunc.endswith(" as Fraction of Total"):
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total = df.sum().sum()
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df = df.astype(total.dtypes) / total
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elif aggfunc.endswith(" as Fraction of Columns"):
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total = df.sum(axis=axis["rows"])
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df = df.astype(total.dtypes).div(total, axis=axis["columns"])
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elif aggfunc.endswith(" as Fraction of Rows"):
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total = df.sum(axis=axis["columns"])
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df = df.astype(total.dtypes).div(total, axis=axis["rows"])
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if show_rows_total:
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# convert to a MultiIndex to simplify logic
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if not isinstance(df.columns, pd.MultiIndex):
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df.columns = pd.MultiIndex.from_tuples([(str(i),) for i in df.columns])
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# add subtotal for each group and overall total; we start from the
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# overall group, and iterate deeper into subgroups
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groups = df.columns
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for level in range(df.columns.nlevels):
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subgroups = {group[:level] for group in groups}
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for subgroup in subgroups:
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slice_ = df.columns.get_loc(subgroup)
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subtotal = pivot_v2_aggfunc_map[aggfunc](df.iloc[:, slice_], axis=1)
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depth = df.columns.nlevels - len(subgroup) - 1
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total = metric_name if level == 0 else "Subtotal"
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subtotal_name = tuple([*subgroup, total, *([""] * depth)])
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# insert column after subgroup
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df.insert(int(slice_.stop), subtotal_name, subtotal)
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if rows and show_columns_total:
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# convert to a MultiIndex to simplify logic
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if not isinstance(df.index, pd.MultiIndex):
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df.index = pd.MultiIndex.from_tuples([(str(i),) for i in df.index])
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# add subtotal for each group and overall total; we start from the
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# overall group, and iterate deeper into subgroups
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groups = df.index
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for level in range(df.index.nlevels):
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subgroups = {group[:level] for group in groups}
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for subgroup in subgroups:
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slice_ = df.index.get_loc(subgroup)
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subtotal = pivot_v2_aggfunc_map[aggfunc](
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df.iloc[slice_, :].apply(pd.to_numeric), axis=0
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)
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depth = df.index.nlevels - len(subgroup) - 1
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total = metric_name if level == 0 else "Subtotal"
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subtotal.name = tuple([*subgroup, total, *([""] * depth)])
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# insert row after subgroup
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df = pd.concat(
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[df[: slice_.stop], subtotal.to_frame().T, df[slice_.stop :]]
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)
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# if we want to apply the metrics on the rows we need to pivot the
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# dataframe back
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if apply_metrics_on_rows:
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df = df.T
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return df
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@ -125,61 +216,49 @@ def pivot_table_v2( # pylint: disable=too-many-branches
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if form_data.get("granularity_sqla") == "all" and DTTM_ALIAS in df:
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del df[DTTM_ALIAS]
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# TODO (betodealmeida): implement metricsLayout
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metrics = [get_metric_name(m) for m in form_data["metrics"]]
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aggregate_function = form_data.get("aggregateFunction", "Sum")
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groupby = form_data.get("groupbyRows") or []
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columns = form_data.get("groupbyColumns") or []
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if form_data.get("transposePivot"):
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groupby, columns = columns, groupby
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df = df.pivot_table(
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index=groupby,
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columns=columns,
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values=metrics,
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aggfunc=pivot_v2_aggfunc_map[aggregate_function],
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margins=True,
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return pivot_df(
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df,
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rows=form_data.get("groupbyRows") or [],
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columns=form_data.get("groupbyColumns") or [],
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metrics=[get_metric_name(m) for m in form_data["metrics"]],
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aggfunc=form_data.get("aggregateFunction", "Sum"),
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transpose_pivot=bool(form_data.get("transposePivot")),
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combine_metrics=bool(form_data.get("combineMetric")),
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show_rows_total=bool(form_data.get("rowTotals")),
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show_columns_total=bool(form_data.get("colTotals")),
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apply_metrics_on_rows=form_data.get("metricsLayout") == "ROWS",
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)
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# The pandas `pivot_table` method either brings both row/column
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# totals, or none at all. We pass `margin=True` to get both, and
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# remove any dimension that was not requests.
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if columns and not form_data.get("rowTotals"):
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df.drop(df.columns[len(df.columns) - 1], axis=1, inplace=True)
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if groupby and not form_data.get("colTotals"):
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df = df[:-1]
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# Compute fractions, if needed. If `colTotals` or `rowTotals` are
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# present we need to adjust for including them in the sum
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if aggregate_function.endswith(" as Fraction of Total"):
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total = df.sum().sum()
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df = df.astype(total.dtypes) / total
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if form_data.get("colTotals"):
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df *= 2
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if form_data.get("rowTotals"):
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df *= 2
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elif aggregate_function.endswith(" as Fraction of Columns"):
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total = df.sum(axis=0)
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df = df.astype(total.dtypes).div(total, axis=1)
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if form_data.get("colTotals"):
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df *= 2
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elif aggregate_function.endswith(" as Fraction of Rows"):
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total = df.sum(axis=1)
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df = df.astype(total.dtypes).div(total, axis=0)
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if form_data.get("rowTotals"):
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df *= 2
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def pivot_table(df: pd.DataFrame, form_data: Dict[str, Any]) -> pd.DataFrame:
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"""
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Pivot table (v1).
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"""
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if form_data.get("granularity") == "all" and DTTM_ALIAS in df:
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del df[DTTM_ALIAS]
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# Display metrics side by side with each column
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if form_data.get("combineMetric"):
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df = df.stack(0).unstack().reindex(level=-1, columns=metrics)
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# v1 func names => v2 func names
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func_map = {
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"sum": "Sum",
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"mean": "Average",
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"min": "Minimum",
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"max": "Maximum",
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"std": "Sample Standard Deviation",
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"var": "Sample Variance",
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}
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# flatten column names
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df.columns = [
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" ".join(str(name) for name in column) if isinstance(column, tuple) else column
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for column in df.columns
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]
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return df
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return pivot_df(
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df,
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rows=form_data.get("groupby") or [],
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columns=form_data.get("columns") or [],
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metrics=[get_metric_name(m) for m in form_data["metrics"]],
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aggfunc=func_map.get(form_data.get("pandas_aggfunc", "sum"), "Sum"),
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transpose_pivot=bool(form_data.get("transpose_pivot")),
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combine_metrics=bool(form_data.get("combine_metric")),
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show_rows_total=bool(form_data.get("pivot_margins")),
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show_columns_total=bool(form_data.get("pivot_margins")),
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apply_metrics_on_rows=False,
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)
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post_processors = {
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@ -203,6 +282,14 @@ def apply_post_process(
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df = pd.read_csv(StringIO(query["data"]))
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processed_df = post_processor(df, form_data)
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# flatten column names
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processed_df.columns = [
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" ".join(str(name) for name in column).strip()
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if isinstance(column, tuple)
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else column
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for column in processed_df.columns
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]
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buf = StringIO()
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processed_df.to_csv(buf)
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buf.seek(0)
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@ -18,7 +18,9 @@
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import copy
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from typing import Any, Dict
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from superset.charts.post_processing import apply_post_process
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import pandas as pd
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from superset.charts.post_processing import apply_post_process, pivot_df
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from superset.utils.core import GenericDataType, QueryStatus
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RESULT: Dict[str, Any] = {
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@ -149,7 +151,8 @@ LIMIT 50000;
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"Births PA",
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"Births TX",
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"Births other",
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"Births All",
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"Births Subtotal",
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"Total (Sum)",
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],
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"coltypes": [
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GenericDataType.NUMERIC,
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@ -164,11 +167,12 @@ LIMIT 50000;
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GenericDataType.NUMERIC,
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GenericDataType.NUMERIC,
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GenericDataType.NUMERIC,
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GenericDataType.NUMERIC,
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],
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"data": """gender,Births CA,Births FL,Births IL,Births MA,Births MI,Births NJ,Births NY,Births OH,Births PA,Births TX,Births other,Births All
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boy,5430796,1968060,2357411,1285126,1938321,1486126,3543961,2376385,2390275,3311985,22044909,48133355
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girl,3567754,1312593,1614427,842146,1326229,992702,2280733,1622814,1615383,2313186,15058341,32546308
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All,8998550,3280653,3971838,2127272,3264550,2478828,5824694,3999199,4005658,5625171,37103250,80679663
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"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)
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boy,5430796,1968060,2357411,1285126,1938321,1486126,3543961,2376385,2390275,3311985,22044909,48133355,48133355
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girl,3567754,1312593,1614427,842146,1326229,992702,2280733,1622814,1615383,2313186,15058341,32546308,32546308
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Total (Sum),8998550,3280653,3971838,2127272,3264550,2478828,5824694,3999199,4005658,5625171,37103250,80679663,80679663
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""",
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"applied_filters": [],
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"rejected_filters": [],
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@ -199,7 +203,7 @@ def test_pivot_table_v2():
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"optionName": "metric_11",
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}
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],
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"metricsLayout": "ROWS",
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"metricsLayout": "COLUMNS",
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"rowOrder": "key_a_to_z",
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"rowTotals": True,
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"row_limit": 50000,
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@ -237,28 +241,746 @@ LIMIT 50000;
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"status": QueryStatus.SUCCESS,
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"stacktrace": None,
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"rowcount": 12,
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"colnames": ["All Births", "boy Births", "girl Births"],
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"colnames": [
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"boy Births",
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"boy Subtotal",
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"girl Births",
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"girl Subtotal",
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"Total (Sum as Fraction of Rows)",
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],
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"coltypes": [
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GenericDataType.NUMERIC,
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GenericDataType.NUMERIC,
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GenericDataType.NUMERIC,
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GenericDataType.NUMERIC,
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GenericDataType.NUMERIC,
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],
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"data": """state,All Births,boy Births,girl Births
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All,1.0,0.5965983645717509,0.40340163542824914
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CA,1.0,0.6035190113962805,0.3964809886037195
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FL,1.0,0.5998988615985903,0.4001011384014097
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IL,1.0,0.5935315085862012,0.40646849141379887
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MA,1.0,0.6041192663655611,0.3958807336344389
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MI,1.0,0.5937482960898133,0.4062517039101867
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NJ,1.0,0.5995276800165239,0.40047231998347604
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NY,1.0,0.6084372844307357,0.39156271556926425
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OH,1.0,0.5942152416021308,0.40578475839786915
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PA,1.0,0.596724682935987,0.40327531706401293
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TX,1.0,0.5887794344385264,0.41122056556147357
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other,1.0,0.5941503507105172,0.40584964928948275
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"data": """,boy Births,boy Subtotal,girl Births,girl Subtotal,Total (Sum as Fraction of Rows)
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CA,0.6035190113962805,0.6035190113962805,0.3964809886037195,0.3964809886037195,1.0
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FL,0.5998988615985903,0.5998988615985903,0.4001011384014097,0.4001011384014097,1.0
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IL,0.5935315085862012,0.5935315085862012,0.40646849141379887,0.40646849141379887,1.0
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MA,0.6041192663655611,0.6041192663655611,0.3958807336344389,0.3958807336344389,1.0
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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()
|
||||
)
|
||||
|
|
|
|||
Loading…
Reference in New Issue