villebro commented on a change in pull request #9427: feat: Add post processing to QueryObject URL: https://github.com/apache/incubator-superset/pull/9427#discussion_r405959966
########## File path: superset/utils/pandas_postprocessing.py ########## @@ -0,0 +1,323 @@ +# 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. +from functools import partial +from typing import Any, Dict, KeysView, List, Optional, Sequence, Union + +import numpy as np +from flask_babel import gettext as _ +from pandas import DataFrame, NamedAgg + +from superset.exceptions import ChartDataValidationError + +SUPPORTED_NUMPY_FUNCTIONS = ( + "average", + "argmin", + "argmax", + "cumsum", + "cumprod", + "max", + "mean", + "median", + "nansum" "nanmin" "nanmax" "nanmean", + "nanmedian", + "min", + "percentile", + "prod", + "product", + "std", + "sum", + "var", +) + + +def _validate_columns( + df: DataFrame, columns: Union[KeysView[str], Sequence[str]] +) -> None: + """ + Ensure that all columns are present in the DataFrame + + :param df: Base DataFrame + :param columns: column existence to check in `df` + """ + if not all(elem in df.columns.tolist() for elem in columns): + raise ChartDataValidationError( + _( + "Referenced columns not available in DataFrame. " + "Columns in DataFrame: %(df_cols)s. Referenced columns: %(columns)s", + df_cols=list(df.columns), + columns=list(columns), + ) + ) + + +def _get_aggregate_funcs(aggregates: Dict[str, Dict[str, Any]],) -> Dict[str, NamedAgg]: + """ + Converts a set of aggregate config objects into functions that pandas can use as + aggregators. Currently only numpy aggregators are supported. + + :param aggregates: Mapping from column name to aggregat config. + :return: Mapping from metric name to function that takes a single input argument. + """ + agg_funcs: Dict[str, NamedAgg] = {} + for name, agg_obj in aggregates.items(): + column = agg_obj.get("column", name) + operator = agg_obj.get("operator") or "sum" + if operator not in SUPPORTED_NUMPY_FUNCTIONS: + raise ChartDataValidationError("Unsupported numpy function: %") + func = getattr(np, operator) + options = agg_obj.get("options", {}) + agg_funcs[name] = NamedAgg(column=column, aggfunc=partial(func, **options)) + + return agg_funcs + + +def _append_columns( + base_df: DataFrame, append_df: DataFrame, columns: Dict[str, str] +) -> DataFrame: + """ + Function for adding columns from one DataFrame to another DataFrame. Calls the + assign method, which overwrites the original column in `base_df` if the column + already exists, and appends the column if the name is not defined. + + :param base_df: DataFrame which to use as the base + :param append_df: DataFrame from which to select data. + :param columns: columns on which to append, mapping source column to + target column. For instance, `{'y': 'y'}` will replace the values in + column `y` in `base_df` with the values in `y` in `append_df`, + while `{'y': 'y2'}` will add a column `y2` to `base_df` based + on values in column `y` in `append_df`, leaving the original column `y` + in `base_df` unchanged. + :return: new DataFrame with combined data from `base_df` and `append_df` + """ + return base_df.assign( + **{ + target: append_df[append_df.columns[idx]] + for idx, target in enumerate(columns.values()) + } + ) + + +def pivot( # pylint: disable=too-many-arguments + df: DataFrame, + index: List[str], + columns: List[str], + aggregates: Dict[str, Dict[str, Any]], + metric_fill_value: Optional[Any] = None, + column_fill_value: Optional[str] = None, + drop_missing_columns: Optional[bool] = True, + combine_value_with_metric=False, + marginal_distributions: Optional[bool] = None, + marginal_distribution_name: Optional[str] = None, +) -> DataFrame: + """ + Perform a pivot operation on a DataFrame. + + :param df: Object on which pivot operation will be performed + :param index: Columns to group by on the table index (=rows) + :param columns: Columns to group by on the table columns + :param metric_fill_value: Value to replace missing values with + :param column_fill_value: Value to replace missing pivot columns with + :param drop_missing_columns: Do not include columns whose entries are all missing + :param combine_value_with_metric: Display metrics side by side within each column, + as opposed to each column being displayed side by side for each metric. + :param aggregates: A mapping from aggregate column name to the the aggregate + config. + :param marginal_distributions: Add totals for row/column. Default to False + :param marginal_distribution_name: Name of row/column with marginal distribution. + Default to 'All'. + :return: A pivot table + :raises ChartDataValidationError: If the request in incorrect + """ + _validate_columns(df, index) + _validate_columns(df, columns) + _validate_columns(df, aggregates.keys()) + if not index: + raise ChartDataValidationError(_("Pivot operation requires at least one index")) + if not columns: + raise ChartDataValidationError( + _("Pivot operation requires at least one column") + ) + if not aggregates: + raise ChartDataValidationError(_("Pivot operation specifying aggregates")) + + if column_fill_value: + df[columns] = df[columns].fillna(value=column_fill_value) + + aggregate_funcs = _get_aggregate_funcs(aggregates) + + # TODO (villebro): Pandas 1.0.3 doesn't yet support NamedAgg in pivot_table. + # Remove once support is added. + aggfunc = {na.column: na.aggfunc for na in aggregate_funcs.values()} + + df = df.pivot_table( + values=aggfunc.keys(), + index=index, + columns=columns, + aggfunc=aggfunc, + fill_value=metric_fill_value, + dropna=drop_missing_columns, + margins=marginal_distributions, + margins_name=marginal_distribution_name, + ) + + if combine_value_with_metric: + df = df.stack(0).unstack() + + return df + + +def aggregate( + df: DataFrame, groupby: List[str], aggregates: Dict[str, Dict[str, Any]] +) -> DataFrame: + """ + Apply aggregations to a DataFrame. + + :param df: Object to aggregate. + :param groupby: columns to aggregate + :param aggregates: A mapping from metric column to the function used to + aggregate values. + :raises ChartDataValidationError: If the request in incorrect + """ + _validate_columns(df, groupby) + aggregates = aggregates or {} + aggregate_funcs = _get_aggregate_funcs(aggregates) + return df.groupby(by=groupby).agg(**aggregate_funcs).reset_index() + + +def sort(df: DataFrame, columns: Dict[str, bool]) -> DataFrame: + """ + Sort a DataFrame. + + :param df: DataFrame to sort. + :param columns: columns by by which to sort. The key specifies the column name, + value specifies if sorting in ascending order. + :return: Sorted DataFrame + :raises ChartDataValidationError: If the request in incorrect + """ + _validate_columns(df, columns.keys()) + return df.sort_values(by=list(columns.keys()), ascending=list(columns.values())) + + +def rolling( # pylint: disable=too-many-arguments + df: DataFrame, + columns: Dict[str, str], + rolling_type: str, + window: int, + center: bool = False, + win_type: Optional[str] = None, + min_periods: Optional[int] = None, +) -> DataFrame: + """ + Apply a rolling window on the dataset. See the Pandas docs for further details: + https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.rolling.html + + :param df: DataFrame on which the rolling period will be based. + :param columns: columns on which to perform rolling, mapping source column to + target column. For instance, `{'y': 'y'}` will replace the column `y` with + the rolling value in `y`, while `{'y': 'y2'}` will add a column `y2` based + on rolling values calculated from `y`, leaving the original column `y` + unchanged. + :param rolling_type: Type of rolling window. Any numpy function will work. + :param center: Should the label be at the center of the window. + :param win_type: Type of window function. + :param window: Size of the window. + :param min_periods: + :return: DataFrame with the rolling columns + :raises ChartDataValidationError: If the request in incorrect + """ + _validate_columns(df, columns.keys()) + df_rolling = df[columns.keys()] + if not hasattr(df_rolling, rolling_type): + raise ChartDataValidationError( + _("Unsupported rolling_type: %(type)s", type=rolling_type) + ) + kwargs: Dict[str, Union[str, int]] = {} + if not window: + raise ChartDataValidationError(_("Undefined window for rolling operation")) + + kwargs["window"] = window + if min_periods is not None: + kwargs["min_periods"] = min_periods + if center is not None: + kwargs["center"] = center + if win_type is not None: + kwargs["win_type"] = win_type + + df_rolling = df_rolling.rolling(**kwargs) + df_rolling = getattr(df_rolling, rolling_type)() + df = _append_columns(df, df_rolling, columns) + if min_periods: + df = df[min_periods:] + return df + + +def select(df: DataFrame, columns: Dict[str, str],) -> DataFrame: + """ + Only select a subset of columns in the original dataset. Can be useful for + removing unnecessary intermediate results, renaming and reordering columns. + + :param df: DataFrame on which the rolling period will be based. + :param columns: Columns on which to perform dff, mapping the + column name to its alias. For instance, `{'y': 'y'}` will return + a DataFrame with only the contents of column `y`, + while `{'y': 'y2'}` return a DataFrame with the column `y2` + containing the values from column `y`. + :return: Subset of columns in original DataFrame + :raises ChartDataValidationError: If the request in incorrect + """ + _validate_columns(df, columns.keys()) + return df[columns.keys()].rename(columns=columns) Review comment: Separating rename from columns is probably a good idea, as always requiring a `Dict[str, str]` even for selecting (=dropping or repordering columns) isn't really intuitive. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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