Source code for plotnine.stats.stat_summary_bin

import numpy as np
import pandas as pd

from ..utils import groupby_apply
from ..doctools import document
from ..exceptions import PlotnineWarning
from ..scales.scale import scale_discrete
from .binning import fuzzybreaks
from .stat_summary import make_summary_fun
from .stat import stat

[docs]@document class stat_summary_bin(stat): """ Summarise y values at x intervals {usage} Parameters ---------- {common_parameters} binwidth : float or tuple, optional (default: None) The width of the bins. The default is to use bins bins that cover the range of the data. You should always override this value, exploring multiple widths to find the best to illustrate the stories in your data. bins : int or tuple, optional (default: 30) Number of bins. Overridden by binwidth. breaks : array-like(s), optional (default: None) Bin boundaries. This supercedes the ``binwidth``, ``bins`` and ``boundary`` arguments. boundary : float or tuple, optional (default: None) A boundary between two bins. As with center, things are shifted when boundary is outside the range of the data. For example, to center on integers, use :py:`width=1` and :py:`boundary=0.5`, even if 1 is outside the range of the data. At most one of center and boundary may be specified. fun_data : str or function, optional One of :py:`['mean_cl_boot', 'mean_cl_normal', 'mean_sdl', 'median_hilow', 'mean_se']` or any function that takes a array and returns a dataframe with three rows indexed as ``y``, ``ymin`` and ``ymax``. Defaults to :py:`'mean_se'`. fun_y : function, optional (default: None) Any function that takes an array-like and returns a single value fun_ymax : function, optional (default: None) Any function that takes an array-like and returns a single value fun_args : dict, optional (default: None) Arguments to any of the functions. Provided the names of the arguments of the different functions are in not conflict, the arguments will be assigned to the right functions. If there is a conflict, create a wrapper function that resolves the ambiguity in the argument names. random_state : int or ~numpy.random.RandomState, optional Seed or Random number generator to use. If ``None``, then numpy global generator :class:`numpy.random` is used. Notes ----- The *binwidth*, *bins*, *breaks* and *bounary* arguments can be a tuples with two values (``(xaxis-value, yaxis-value)``) of the required type. See Also -------- plotnine.geoms.geom_pointrange """ _aesthetics_doc = """ {aesthetics_table} .. rubric:: Options for computed aesthetics :: 'bin' # bin identifier 'width' # bin width 'ymin' # ymin computed by the summary function 'ymax' # ymax computed by the summary function Calculated aesthetics are accessed using the `after_stat` function. e.g. :py:`after_stat('ymin')`. """ REQUIRED_AES = {'x', 'y'} DEFAULT_PARAMS = {'geom': 'pointrange', 'position': 'identity', 'na_rm': False, 'bins': 30, 'breaks': None, 'binwidth': None, 'boundary': None, 'fun_data': None, 'fun_y': None, 'fun_ymin': None, 'fun_ymax': None, 'fun_args': None, 'random_state': None} CREATES = {'bin', 'width', 'ymin', 'ymax'} def setup_params(self, data): keys = ('fun_data', 'fun_y', 'fun_ymin', 'fun_ymax') if not any(self.params[k] for k in keys): PlotnineWarning( "No summary function, supplied, defaulting to mean_se()" ) self.params['fun_data'] = 'mean_se' if self.params['fun_args'] is None: self.params['fun_args'] = {} if 'random_state' not in self.params['fun_args']: if self.params['random_state']: random_state = self.params['random_state'] if random_state is None: random_state = np.random elif isinstance(random_state, int): random_state = np.random.RandomState(random_state) self.params['fun_args']['random_state'] = random_state return self.params @classmethod def compute_group(cls, data, scales, **params): bins = params['bins'] breaks = params['breaks'] binwidth = params['binwidth'] boundary = params['boundary'] func = make_summary_fun(params['fun_data'], params['fun_y'], params['fun_ymin'], params['fun_ymax'], params['fun_args']) breaks = fuzzybreaks(scales.x, breaks, boundary, binwidth, bins) data['bin'] = pd.cut(data['x'], bins=breaks, labels=False, include_lowest=True) def func_wrapper(data): """ Add `bin` column to each summary result. """ result = func(data) result['bin'] = data['bin'].iloc[0] return result # This is a plyr::ddply out = groupby_apply(data, 'bin', func_wrapper) centers = (breaks[:-1] + breaks[1:]) * 0.5 bin_centers = centers[out['bin'].values] out['x'] = bin_centers out['bin'] += 1 if isinstance(scales.x, scale_discrete): out['width'] = 0.9 else: out['width'] = np.diff(breaks)[bins-1] return out