Source code for panel.pane.perspective

from __future__ import annotations

import datetime as dt
import sys

from enum import Enum
from functools import partial
from typing import (
    TYPE_CHECKING, Callable, ClassVar, List, Mapping, Optional, Type,

import numpy as np
import param

from bokeh.models import ColumnDataSource, ImportedStyleSheet
from pyviz_comms import JupyterComm

from import CDN_DIST
from import state
from ..reactive import ReactiveData
from ..util import datetime_types, lazy_load
from ..viewable import Viewable
from .base import ModelPane

    from bokeh.document import Document
    from bokeh.model import Model
    from pyviz_comms import Comm

    from ..model.perspective import PerspectiveClickEvent

DEFAULT_THEME = "material"

    'material', 'material-dark', 'monokai', 'solarized', 'solarized-dark', 'vaporwave'

[docs]class Plugin(Enum): """The plugins (grids/charts) available in Perspective. Pass these into the `plugin` arg in `PerspectiveWidget` or `PerspectiveViewer`. """ HYPERGRID = "hypergrid" # hypergrid GRID = "datagrid" # hypergrid YBAR_D3 = "d3_y_bar" # d3fc XBAR_D3 = "d3_x_bar" # d3fc XYLINE_D3 = "d3_xy_line" # d3fc YLINE_D3 = "d3_y_line" # d3fc YAREA_D3 = "d3_y_area" # d3fc YSCATTER_D3 = "d3_y_scatter" # d3fc XYSCATTER_D3 = "d3_xy_scatter" # d3fc TREEMAP_D3 = "d3_treemap" # d3fc SUNBURST_D3 = "d3_sunburst" # d3fc HEATMAP_D3 = "d3_heatmap" # d3fc CANDLESTICK = "d3_candlestick" # d3fc CANDLESTICK_D3 = "d3_candlestick" # d3fc OHLC = "d3_ohlc" # d3fc OHLC_D3 = "d3_ohlc" # d3fc
[docs] @staticmethod def options(): """ Returns the list of options of the PerspectiveViewer, like Hypergrid, Grid etc. Returns ------- options: list A list of available options """ return list(c.value for c in Plugin)
[docs]def deconstruct_pandas(data, kwargs=None): """ Given a dataframe, flatten it by resetting the index and memoizing the pivots that were applied. This code was copied from the Perspective repository and is reproduced under Apache 2.0 license. See the original at: Arguments --------- data: (pandas.dataframe) A Pandas DataFrame to parse Returns ------- data: pandas.DataFrame A flattened version of the DataFrame kwargs: dict A dictionary containing optional members `columns`, `group_by`, and `split_by`. """ import pandas as pd kwargs = kwargs or {} kwargs = {"columns": [], "group_by": [], "split_by": []} if isinstance(data.index, pd.PeriodIndex): data.index = data.index.to_timestamp() if isinstance(data, pd.DataFrame): if hasattr(pd, "CategoricalDtype"): for k, v in data.dtypes.items(): if isinstance(v, pd.CategoricalDtype): data[k] = data[k].astype(str) if ( isinstance(data, pd.DataFrame) and isinstance(data.columns, pd.MultiIndex) and isinstance(data.index, pd.MultiIndex) ): # Row and col pivots kwargs["group_by"].extend([str(c) for c in data.index.names]) # Two strategies if None in data.columns.names: # In this case, we need to extract the column names from the row # e.g. pt = pd.pivot_table(df, values = ['Discount','Sales'], index=['Country','Region'], columns=["State","Quantity"]) # Table will be # Discount Sales # State Alabama Alaska ... Alabama Alaska ... # Quantity 150 350 ... 300 500 # Country Region # US Region 0 ... # US Region 1 # # We need to transform this to: # group_by = ['Country', 'Region'] # split_by = ['State', 'Quantity'] # columns = ['Discount', 'Sales'] existent = kwargs["group_by"] + data.columns.names for c in data.columns.names: if c is not None: kwargs["split_by"].append(c) data = data.stack() data = pd.DataFrame(data).reset_index() for new_column in data.columns: if new_column not in existent: kwargs["columns"].append(new_column) else: # In this case, we have no need as the values is just a single entry # e.g. pt = pd.pivot_table(df, values = 'Discount', index=['Country','Region'], columns = ['Category', 'Segment']) for _ in kwargs["group_by"]: # unstack row pivots data = data.unstack() data = pd.DataFrame(data) # this rather weird loop is to map existing None columns into # levels, e.g. in the `else` block above, to reconstruct # the "Discount" name. IDK if this is stored or if the name is # lots, so we'll just call it 'index', 'index-1', ... i = 0 new_names = list(data.index.names) for j, val in enumerate(data.index.names): if val is None: new_names[j] = "index" if i == 0 else "index-{}".format(i) i += 1 # kwargs['group_by'].append(str(new_names[j])) else: if str(val) not in kwargs["group_by"]: kwargs["split_by"].append(str(val)) # Finally, remap any values columns to have column name 'value' data.index.names = new_names data = data.reset_index() # copy data.columns = [ str(c) if c in ["index"] + kwargs["group_by"] + kwargs["split_by"] + kwargs["columns"] else "value" for c in data.columns ] kwargs["columns"].extend( [ "value" for c in data.columns if c not in ["index"] + kwargs["group_by"] + kwargs["split_by"] + kwargs["columns"] ] ) elif isinstance(data, pd.DataFrame) and isinstance(data.columns, pd.MultiIndex): # Col pivots if kwargs["group_by"].append(str( push_row_pivot = False else: push_row_pivot = True data = pd.DataFrame(data.unstack()) i = 0 new_names = list(data.index.names) for j, val in enumerate(data.index.names): if val is None: new_names[j] = "index" if i == 0 else "index-{}".format(i) i += 1 if push_row_pivot: kwargs["group_by"].append(str(new_names[j])) else: if str(val) not in kwargs["group_by"]: kwargs["split_by"].append(str(val)) data.index.names = new_names data.columns = [ str(c) if c in ["index"] + kwargs["group_by"] + kwargs["split_by"] else "value" for c in data.columns ] kwargs["columns"].extend( [ "value" for c in data.columns if c not in ["index"] + kwargs["group_by"] + kwargs["split_by"] ] ) elif isinstance(data, pd.DataFrame) and isinstance(data.index, pd.MultiIndex): # Row pivots kwargs["group_by"].extend(list(data.index.names)) data = data.reset_index() # copy if isinstance(data, pd.DataFrame): # flat df if "index" not in [str(c).lower() for c in data.columns]: data = data.reset_index(col_fill="index") if not kwargs["columns"]: # might already be set in row+col pivot df kwargs["columns"].extend([str(c) for c in data.columns]) data.columns = kwargs["columns"] if isinstance(data, pd.Series): # Series flattened = data.reset_index() # copy if isinstance(data, pd.Series): # preserve name from series = # make sure all columns are strings flattened.columns = [str(c) for c in flattened.columns] data = flattened return data, kwargs
[docs]class Perspective(ModelPane, ReactiveData): """ The `Perspective` pane provides an interactive visualization component for large, real-time datasets built on the Perspective project. Reference: :Example: >>> Perspective(df, plugin='hypergrid', theme='material-dark') """ aggregates = param.Dict(default=None, nested_refs=True, doc=""" How to aggregate. For example {"x": "distinct count"}""") columns = param.List(default=None, nested_refs=True, doc=""" A list of source columns to show as columns. For example ["x", "y"]""") editable = param.Boolean(default=True, allow_None=True, doc=""" Whether items are editable.""") expressions = param.List(default=None, nested_refs=True, doc=""" A list of expressions computing new columns from existing columns. For example [""x"+"index""]""") split_by = param.List(default=None, nested_refs=True, doc=""" A list of source columns to pivot by. For example ["x", "y"]""") filters = param.List(default=None, nested_refs=True, doc=""" How to filter. For example [["x", "<", 3],["y", "contains", "abc"]]""") min_width = param.Integer(default=420, bounds=(0, None), doc=""" Minimal width of the component (in pixels) if width is adjustable.""") object = param.Parameter(doc=""" The plot data declared as a dictionary of arrays or a DataFrame.""") group_by = param.List(default=None, doc=""" A list of source columns to group by. For example ["x", "y"]""") selectable = param.Boolean(default=True, allow_None=True, doc=""" Whether items are selectable.""") sort = param.List(default=None, doc=""" How to sort. For example[["x","desc"]]""") plugin = param.ObjectSelector(default=Plugin.GRID.value, objects=Plugin.options(), doc=""" The name of a plugin to display the data. For example hypergrid or d3_xy_scatter.""") plugin_config = param.Dict(default={}, nested_refs=True, doc=""" Configuration for the PerspectiveViewerPlugin.""") toggle_config = param.Boolean(default=True, doc=""" Whether to show the config menu.""") theme = param.ObjectSelector(default='material', objects=THEMES, doc=""" The style of the PerspectiveViewer. For example material-dark""") priority: ClassVar[float | bool | None] = None _bokeh_model: ClassVar[Type[Model] | None] = None _data_params: ClassVar[List[str]] = ['object'] _rename: ClassVar[Mapping[str, str | None]] = { 'selection': None } _updates: ClassVar[bool] = True _stylesheets: ClassVar[List[str]] = [ f'{CDN_DIST}css/perspective-datatable.css' ]
[docs] @classmethod def applies(cls, object): if isinstance(object, dict) and all(isinstance(v, (list, np.ndarray)) for v in object.values()): return 0 if object else None elif 'pandas' in sys.modules: import pandas as pd if isinstance(object, pd.DataFrame): return 0 return False
def __init__(self, object=None, **params): click_handler = params.pop('on_click', None) self._on_click_callbacks = [] super().__init__(object, **params) if click_handler: self.on_click(click_handler) def _get_data(self): if self.object is None: return {}, {} if isinstance(self.object, dict): ncols = len(self.object) df = data = self.object else: df, kwargs = deconstruct_pandas(self.object) ncols = len(df.columns) data = {col: df[col].values for col in df.columns} if kwargs: self.param.update(**{ k: v for k, v in kwargs.items() if getattr(self, k) is None }) cols = set(self._as_digit(c) for c in df) if len(cols) != ncols: raise ValueError("Integer columns must be unique when " "converted to strings.") return df, {str(k): v for k, v in data.items()} def _filter_properties(self, properties): ignored = list(Viewable.param) return [p for p in properties if p not in ignored] def _get_properties(self, doc, source=None): props = super()._get_properties(doc) del props['object'] if props.get('toggle_config'): props['height'] = self.height or 300 else: props['height'] = self.height or 150 if source is None: source = ColumnDataSource(data=self._data) else: = self._data props['source'] = source props['schema'] = schema = {} for col, array in if not isinstance(array, np.ndarray): array = np.asarray(array) kind = array.dtype.kind if kind == 'M': schema[col] = 'datetime' elif kind in 'ui': schema[col] = 'integer' elif kind == 'b': schema[col] = 'boolean' elif kind == 'f': schema[col] = 'float' elif kind in 'sU': schema[col] = 'string' else: if len(array): value = array[0] if isinstance(value, schema[col] = 'date' elif isinstance(value, datetime_types): schema[col] = 'datetime' elif isinstance(value, str): schema[col] = 'string' elif isinstance(value, (float, np.floating)): schema[col] = 'float' elif isinstance(value, (int, np.integer)): schema[col] = 'integer' else: schema[col] = 'string' else: schema[col] = 'string' return props def _get_theme(self, theme, resources=None): from ..models.perspective import THEME_URL theme_url = f'{THEME_URL}{theme}.css' if self._bokeh_model is not None: self._bokeh_model.__css_raw__ = self._bokeh_model.__css_raw__[:3] + [theme_url] return theme_url def _process_param_change(self, params): if 'stylesheets' in params or 'theme' in params: self._get_theme(params.get('theme', self.theme)) css = getattr(self._bokeh_model, '__css__', []) params['stylesheets'] = [ ImportedStyleSheet(url=ss) for ss in css ] + params.get('stylesheets', self.stylesheets) props = super()._process_param_change(params) for p in ('columns', 'group_by', 'split_by'): if props.get(p): props[p] = [None if col is None else str(col) for col in props[p]] if props.get('sort'): props['sort'] = [[str(col), *args] for col, *args in props['sort']] if props.get('filters'): props['filters'] = [[str(col), *args] for col, *args in props['filters']] if props.get('aggregates'): props['aggregates'] = {str(col): agg for col, agg in props['aggregates'].items()} return props def _as_digit(self, col): if self._processed is None or col in self._processed or col is None: return col elif col.isdigit() and int(col) in self._processed: return int(col) return col def _process_property_change(self, msg): msg = super()._process_property_change(msg) for prop in ('columns', 'group_by', 'split_by'): if prop not in msg: continue msg[prop] = [self._as_digit(col) for col in msg[prop]] if msg.get('sort'): msg['sort'] = [[self._as_digit(col), *args] for col, *args in msg['sort']] if msg.get('filters'): msg['filters'] = [[self._as_digit(col), *args] for col, *args in msg['filters']] if msg.get('aggregates'): msg['aggregates'] = {self._as_digit(col): agg for col, agg in msg['aggregates'].items()} return msg def _get_model( self, doc: Document, root: Optional[Model] = None, parent: Optional[Model] = None, comm: Optional[Comm] = None ) -> Model: self._bokeh_model = lazy_load( 'panel.models.perspective', 'Perspective', isinstance(comm, JupyterComm), root ) model = super()._get_model(doc, root, parent, comm) self._register_events('perspective-click', model=model, doc=doc, comm=comm) return model def _update(self, ref: str, model: Model) -> None: model.update(**self._get_properties(model.document, source=model.source)) def _process_event(self, event): if event.event_name == 'perspective-click': for cb in self._on_click_callbacks: state.execute(partial(cb, event), schedule=False)
[docs] def on_click(self, callback: Callable[[PerspectiveClickEvent], None]): """ Register a callback to be executed when any row is clicked. The callback is given a PerspectiveClickEvent declaring the config, column names, and row values of the row that was clicked. Arguments --------- callback: (callable) The callback to run on edit events. """ self._on_click_callbacks.append(callback)