Source code for pyam.statistics

from copy import deepcopy
import numpy as np
import pandas as pd
from pyam import filter_by_meta, META_IDX
from pyam.utils import isstr, islistable


[docs]class Statistics(object): """This class generates descriptive statistics of timeseries data Parameters ---------- df : IamDataFrame an IamDataFrame from which to retrieve meta indicators for grouping or filtering groupby : str or dict a column of `df.meta` to be used for `groupby` or a dictionary of `{column: list}`, where `list` is used for ordering filters : list of tuples arguments for filtering and describing, either `((index, dict)` or `((index[0], index[1]), dict)`; when also using `groupby`, index must have length 2. percentiles : list-like of numbers, optional The percentiles to get from :meth:`pandas.DataFrame.describe()`. All should fall between 0 and 1. The default is `[.25, .5, .75]`, which returns the 25th, 50th, and 75th percentiles. """ def __init__( self, df, groupby=None, filters=None, rows=False, percentiles=[0.25, 0.5, 0.75] ): self.df = df self.idx_depth = None # assing `groupby` settings and check that specifications are valid self.col = None self.groupby = None if isstr(groupby): self.col = groupby self.groupby = {groupby: None} elif isinstance(groupby, dict) and len(groupby) == 1: self.col = list(groupby.keys())[0] self.groupby = groupby self.idx_depth = 2 elif groupby is not None: raise ValueError("arg `{}` not valid `groupby`".format(groupby)) if self.col is not None and self.col not in df.meta.columns: raise ValueError("column `{}` not in `df.meta`".format(self.col)) # if neither groupby nor filters is given, use filters to describe all # and assume that rows are used if groupby is None and filters is None: self.filters = [("", {})] rows = True else: self.filters = filters if filters is not None else [] # set lists to sort index and subindex self._idx = [] if self.col is None else [self.col] self._sub_idx = ( self.groupby[self.col] or self.df[self.col].unique() if self.col is not None else [] ) self._headers, self._subheaders = ([], []) # assing `filters` settings and check that specifications are valid for (idx, _filter) in self.filters: # check that index in tuple is valid if isstr(idx): self._add_to_index(idx) else: if not ( isinstance(idx, tuple) and len(idx) == 2 and isstr(idx[0]) or not isstr(idx[1]) ): raise ValueError("`{}` is not a valid index".format(idx)) self._add_to_index(idx[0], idx[1]) # check that filters in tuple are valid if not isinstance(_filter, dict): raise ValueError("`{}` is not a valid filter".format(_filter)) elif not (set(_filter) - set(META_IDX)).issubset(df.meta): raise ValueError( "column `{}` not in `df.meta`".format( set(_filter) - set(META_IDX) - set(df.meta) ) ) self.stats = None self.rows = [] if rows else None # percentiles for passing to `pandas.describe()` self.percentiles = list(percentiles) self._describe_cols = ( ["count", "mean", "std", "min"] + ["{:.0%}".format(i) for i in self.percentiles] + ["max"] ) def _add_to_index(self, idx, sub_idx=None): # assign index depth if not set if self.idx_depth is None: self.idx_depth = 1 if sub_idx is None else 2 # check that index matches depth if self.groupby is not None and sub_idx is None: msg = "if `groupby` is used, index `{}` must have format `{}`" raise ValueError(msg.format(idx, "(idx0, idx1)")) if self.idx_depth == 1 and sub_idx is not None: raise ValueError( "index depth set to 1, found `({}, {})`".format(idx, sub_idx) ) if self.idx_depth == 2 and sub_idx is None: raise ValueError("index depth set to 2, found `({})`".format(idx)) # append to lists for sorting index if idx not in self._idx: self._idx.append(idx) if self.idx_depth == 2 and sub_idx not in self._sub_idx: self._sub_idx.append(sub_idx) def _add_to_header(self, header, subheader): if header not in self._headers: self._headers.append(header) if islistable(subheader): for s in subheader: if s not in self._subheaders: self._subheaders.append(s) elif subheader not in self._subheaders: self._subheaders.append(subheader)
[docs] def add(self, data, header, row=None, subheader=None): """Filter 'data' by arguments of this Statistics instance, Apply :meth:`pandas.DataFrame.describe()` and format the statistics Parameters ---------- data : pandas.DataFrame or pandas.Series data for which summary statistics should be computed header : str column name for descriptive statistics row : str row name for descriptive statistics (required if :class:`Statistics(rows=True) <Statistics>`) subheader : str, optional column name (level=1) if data is a unnamed :class:`pandas.Series` """ # verify validity of specifications if self.rows is not None and row is None: raise ValueError("row specification required") if self.rows is None and row is not None: raise ValueError("row arg illegal for this `Statistics` instance") if isinstance(data, pd.Series): if subheader is not None: data.name = subheader elif data.name is None: msg = "`data` must be named `pd.Series` or provide `subheader`" raise ValueError(msg) data = pd.DataFrame(data) if self.rows is not None and row not in self.rows: self.rows.append(row) _stats = None # describe with groupby feature if self.groupby is not None: filter_args = dict(data=data, df=self.df, join_meta=True) filter_args.update(self.groupby) _stats = ( filter_by_meta(**filter_args) .groupby(self.col) .describe(percentiles=self.percentiles) ) _stats = pd.concat([_stats], keys=[self.col], names=[""], axis=0) if self.rows: _stats["row"] = row _stats.set_index("row", append=True, inplace=True) _stats.index.names = [""] * 3 if self.rows else [""] * 2 # describe with filter feature for (idx, _filter) in self.filters: filter_args = dict(data=data, df=self.df) filter_args.update(_filter) _stats_f = filter_by_meta(**filter_args).describe( percentiles=self.percentiles ) _stats_f = pd.DataFrame(_stats_f.unstack()).T if self.idx_depth == 1: levels = [[idx]] else: levels = [[idx[0]], [idx[1]]] lvls, lbls = ( (levels, [[0]] * self.idx_depth) if not self.rows else (levels + [[row]], [[0]] * (self.idx_depth + 1)) ) _stats_f.index = pd.MultiIndex(levels=lvls, codes=lbls) _stats = _stats_f if _stats is None else _stats.append(_stats_f) # add header _stats = pd.concat([_stats], keys=[header], names=[""], axis=1) subheader = _stats.columns.get_level_values(1).unique() self._add_to_header(header, subheader) # set statistics if self.stats is None: self.stats = _stats else: self.stats = _stats.combine_first(self.stats)
[docs] def reindex(self, copy=True): """Reindex the summary statistics dataframe""" ret = deepcopy(self) if copy else self ret.stats = ret.stats.reindex(index=ret._idx, level=0) if ret.idx_depth == 2: ret.stats = ret.stats.reindex(index=ret._sub_idx, level=1) if ret.rows is not None: ret.stats = ret.stats.reindex(index=ret.rows, level=ret.idx_depth) ret.stats = ret.stats.reindex(columns=ret._headers, level=0) ret.stats = ret.stats.reindex(columns=ret._subheaders, level=1) ret.stats = ret.stats.reindex(columns=ret._describe_cols, level=2) if copy: return ret
[docs] def summarize( self, center="mean", fullrange=None, interquartile=None, custom_format="{:.2f}" ): """Format the compiled statistics to a concise string output Parameters ---------- center : str, default `mean` what to return as 'center' of the summary: `mean`, `50%`, `median` fullrange : bool, default None return full range of data if True or `fullrange`, `interquartile` and `format_spec` are None interquartile : bool, default None return interquartile range if True custom_format : formatting specifications """ # call `reindex()` to reorder index and columns self.reindex(copy=False) center = "median" if center == "50%" else center if fullrange is None and interquartile is None: fullrange = True return self.stats.apply( format_rows, center=center, fullrange=fullrange, interquartile=interquartile, custom_format=custom_format, axis=1, raw=False, )
# %% auxiliary functions def format_rows( row, center, fullrange=None, interquartile=None, custom_format="{:.2f}" ): """Format a row with `describe()` columns to a concise string""" if (fullrange or 0) + (interquartile or 0) == 1: legend = "{} ({})".format( center, "max, min" if fullrange is True else "interquartile range" ) index = row.index.droplevel(2).drop_duplicates() count_arg = dict(tuples=[("count", "")], names=[None, legend]) else: msg = "displaying multiple range formats simultaneously not supported" raise NotImplementedError(msg) ret = pd.Series( index=pd.MultiIndex.from_tuples(**count_arg).append(index), dtype=float ) row = row.sort_index() center = "50%" if center == "median" else center # get maximum of `count` and write to first entry of return series count = max( [i for i in row.loc[(slice(None), slice(None), "count")] if not np.isnan(i)] ) ret.loc[("count", "")] = ("{:.0f}".format(count)) if count > 1 else "" # set upper and lower for the range upper, lower = ("max", "min") if fullrange is True else ("75%", "25%") # format `describe()` columns to string output for i in index: x = row.loc[i] _count = x["count"] if np.isnan(_count) or _count == 0: s = "" elif _count > 1: s = "{f} ({f}, {f})".format(f=custom_format).format( x[center], x[upper], x[lower] ) elif _count == 1: s = "{f}".format(f=custom_format).format(x["50%"]) # add count of this section as `[]` if different from count_max if 0 < _count < count: s += " [{:.0f}]".format(_count) ret.loc[i] = s return ret