from __future__ import annotations import datetime as dt import uuid from collections.abc import Callable, Mapping from functools import partial from types import FunctionType, MethodType from typing import ( TYPE_CHECKING, Any, ClassVar, Literal, Sequence, TypedDict, cast, ) import numpy as np import param from bokeh.model import Model from bokeh.models import ColumnDataSource, ImportedStyleSheet from bokeh.models.widgets.tables import ( AvgAggregator, CellEditor, CellFormatter, CheckboxEditor, DataCube, DataTable, DateEditor, DateFormatter, GroupingInfo, IntEditor, MaxAggregator, MinAggregator, NumberEditor, NumberFormatter, RowAggregator, StringEditor, StringFormatter, SumAggregator, TableColumn, ) from bokeh.util.serialization import convert_datetime_array from param.parameterized import transform_reference from pyviz_comms import JupyterComm from ..io.resources import CDN_DIST, CSS_URLS from ..io.state import state from ..reactive import Reactive, ReactiveData from ..util import ( BOKEH_GE_3_6, clone_model, datetime_as_utctimestamp, isdatetime, lazy_load, styler_update, updating, ) from ..util.warnings import warn from .base import Widget from .button import Button from .input import TextInput if TYPE_CHECKING: import pandas as pd from bokeh.document import Document from bokeh.models.sources import DataDict from pyviz_comms import Comm from ..models.tabulator import ( CellClickEvent, SelectionEvent, TableEditEvent, ) from ..reactive import TDataColumn class FilterSpec(TypedDict, total=False): headerFilter: str | bool headerFilterParams: dict[str, Any] headerFilterFunc: str headerFilterPlaceholder: str class ColumnSpec(TypedDict, total=False): editable: bool editor: str | CellEditor editorParams: dict[str, Any] field: str frozen: bool headerHozAlign: Literal["center", "left", "right"] headerSort: bool headerTooltip: str hozAlign: Literal["center", "left", "right"] formatter: str | CellFormatter formatterParams: dict[str, Any] sorter: str title: str titleFormatter: str | CellFormatter titleFormatterParams: dict[str, Any] width: str | int class GroupSpec(TypedDict): columns: Sequence[ColumnSpec] title: str def _convert_datetime_array_ignore_list(v): if isinstance(v, np.ndarray): return convert_datetime_array(v) return v class BaseTable(ReactiveData, Widget): aggregators = param.Dict(default={}, nested_refs=True, doc=""" A dictionary mapping from index name to an aggregator to be used for hierarchical multi-indexes (valid aggregators include 'min', 'max', 'mean' and 'sum'). If separate aggregators for different columns are required the dictionary may be nested as `{index_name: {column_name: aggregator}}`""") editors = param.Dict(default={}, nested_refs=True, doc=""" Bokeh CellEditor to use for a particular column (overrides the default chosen based on the type).""") formatters = param.Dict(default={}, nested_refs=True, doc=""" Bokeh CellFormatter to use for a particular column (overrides the default chosen based on the type).""") hierarchical = param.Boolean(default=False, constant=True, doc=""" Whether to generate a hierarchical index.""") row_height = param.Integer(default=40, doc=""" The height of each table row.""") selection = param.List(default=[], doc=""" The currently selected rows of the table.""") show_index = param.Boolean(default=True, doc=""" Whether to show the index column.""") sorters = param.List(default=[], doc=""" A list of sorters to apply during pagination.""") text_align = param.ClassSelector(default={}, nested_refs=True, class_=(dict, str), doc=""" A mapping from column name to alignment or a fixed column alignment, which should be one of 'left', 'center', 'right'.""") titles = param.Dict(default={}, nested_refs=True, doc=""" A mapping from column name to a title to override the name with.""") widths = param.ClassSelector(default={}, nested_refs=True, class_=(dict, int), doc=""" A mapping from column name to column width or a fixed column width.""") value = param.Parameter(default=None) _data_params: ClassVar[list[str]] = ['value'] _manual_params: ClassVar[list[str]] = [ 'formatters', 'editors', 'widths', 'titles', 'value', 'show_index' ] _rename: ClassVar[Mapping[str, str | None]] = { 'hierarchical': None, 'name': None, 'selection': None } __abstract = True def __init__(self, value=None, **params): self._renamed_cols = {} self._filters = [] self._index_mapping = {} self._edited_indexes = [] super().__init__(value=value, **params) self._internal_callbacks.extend([ self.param.watch(self._setup_on_change, ['editors', 'formatters']), self.param._watch(self._reset_selection, ['value'], precedence=-1) ]) self.param.trigger('editors') self.param.trigger('formatters') @param.depends('value', watch=True, on_init=True) def _compute_renamed_cols(self): if self.value is None: self._renamed_cols.clear() return self._renamed_cols = { ('_'.join(col) if isinstance(col, tuple) else str(col)) if str(col) != col else col: col for col in self._get_fields() } def _reset_selection(self, event): if event.type == 'triggered' and self._updating: return if self._indexes_changed(event.old, event.new): selection = [] for sel in self.selection: idx = event.old.index[sel] try: new = event.new.index.get_loc(idx) selection.append(new) except KeyError: pass self.selection = selection def _indexes_changed(self, old, new): """ Comparator that checks whether DataFrame indexes have changed. If indexes and length are unchanged we assume we do not have to reset various settings including expanded rows, scroll position, pagination etc. """ if type(old) is not type(new) or isinstance(new, dict) or len(old) != len(new): return True return (old.index != new.index).any() @property def _length(self): return len(self._processed) def _validate(self, *events: param.parameterized.Event): if self.value is None: return cols = self.value.columns if len(cols) != len(cols.drop_duplicates()): raise ValueError('Cannot display a pandas.DataFrame with ' 'duplicate column names.') def _get_fields(self) -> list[str]: indexes = self.indexes col_names = [] if self.value is None else list(self.value.columns) if not self.hierarchical or len(indexes) == 1: col_names = indexes + col_names else: col_names = indexes[-1:] + col_names return col_names def _get_columns(self) -> list[TableColumn]: if self.value is None: return [] indexes = self.indexes fields = self._get_fields() df = self.value.reset_index() if len(indexes) > 1 else self.value return self._get_column_definitions(fields, df) def _get_column_definitions(self, col_names: list[str], df: pd.DataFrame) -> list[TableColumn]: import pandas as pd indexes = self.indexes columns = [] for col in col_names: if col in df.columns: data: pd.Series | pd.Index = df[col] elif col in self.indexes: if len(self.indexes) == 1: data = df.index else: data = df.index.get_level_values(self.indexes.index(col)) if isinstance(data, pd.DataFrame): raise ValueError("DataFrame contains duplicate column names.") col_kwargs = {} kind = data.dtype.kind editor: CellEditor formatter: CellFormatter | None = self.formatters.get(col) if kind == 'i': editor = IntEditor() elif kind == 'b': editor = CheckboxEditor() elif kind == 'f': editor = NumberEditor() elif isdatetime(data) or kind == 'M': editor = DateEditor() else: editor = StringEditor() if col in self.editors and not isinstance(self.editors[col], (dict, str)): editor = self.editors[col] if isinstance(editor, CellEditor): editor = clone_model(editor) if col in indexes or editor is None: editor = CellEditor() if formatter is None or isinstance(formatter, (dict, str)): if kind == 'i': formatter = NumberFormatter(text_align='right') elif kind == 'b': formatter = StringFormatter(text_align='center') elif kind == 'f': formatter = NumberFormatter(format='0,0.0[00000]', text_align='right') elif isdatetime(data) or kind == 'M': if len(data) and isinstance(data.values[0], dt.date): date_format = '%Y-%m-%d' else: date_format = '%Y-%m-%d %H:%M:%S' formatter = DateFormatter(format=date_format, text_align='right') else: params = {} if BOKEH_GE_3_6: params['null_format'] = '' formatter = StringFormatter(**params) default_text_align = True else: if isinstance(formatter, CellFormatter): formatter = clone_model(formatter) if formatter and hasattr(formatter, 'text_align'): default_text_align = type(formatter).text_align.class_default(formatter) == formatter.text_align else: default_text_align = True if not formatter or not hasattr(formatter, 'text_align'): pass elif isinstance(self.text_align, str): formatter.text_align = self.text_align if not default_text_align: msg = f"The 'text_align' in Tabulator.formatters[{col!r}] is overridden by Tabulator.text_align" warn(msg, RuntimeWarning) elif col in self.text_align: formatter.text_align = self.text_align[col] if not default_text_align: msg = f"The 'text_align' in Tabulator.formatters[{col!r}] is overridden by Tabulator.text_align[{col!r}]" warn(msg, RuntimeWarning) elif col in self.indexes: formatter.text_align = 'left' if isinstance(self.widths, int): col_kwargs['width'] = self.widths elif str(col) in self.widths and isinstance(self.widths.get(str(col)), int): col_kwargs['width'] = self.widths.get(str(col)) else: col_kwargs['width'] = 0 col_name = '_'.join(col) if isinstance(col, tuple) else col title = self.titles.get(col, str(col_name)) if col in indexes and len(indexes) > 1 and self.hierarchical: title = 'Index: {}'.format(' | '.join(indexes)) elif col in self.indexes and col.startswith('level_'): title = '' column = TableColumn(field=str(col_name), title=title, editor=editor, formatter=formatter, **col_kwargs) columns.append(column) return columns def _setup_on_change(self, *events: param.parameterized.Event): for event in events: self._process_on_change(event) def _process_on_change(self, event: param.parameterized.Event): old, new = event.old, event.new for model in (old if isinstance(old, dict) else {}).values(): if not isinstance(model, (CellEditor, CellFormatter)): continue change_fn = self._editor_change if isinstance(model, CellEditor) else self._formatter_change for prop in (model.properties() - Model.properties()): try: model.remove_on_change(prop, change_fn) except ValueError: pass for model in (new if isinstance(new, dict) else {}).values(): if not isinstance(model, (CellEditor, CellFormatter)): continue change_fn = self._editor_change if isinstance(model, CellEditor) else self._formatter_change for prop in (model.properties() - Model.properties()): model.on_change(prop, change_fn) def _editor_change(self, attr: str, new: Any, old: Any): self.param.trigger('editors') def _formatter_change(self, attr: str, new: Any, old: Any): self.param.trigger('formatters') def _update_index_mapping(self): if self._processed is None or isinstance(self._processed, list) and not self._processed: self._index_mapping = {} return self._index_mapping = { i: index for i, index in enumerate(self._processed.index) } @updating def _update_cds(self, *events: param.parameterized.Event): self._processed, data = self._get_data() self._update_index_mapping() self._data = {k: _convert_datetime_array_ignore_list(v) for k, v in data.items()} named_events = {e.name: e for e in events} msg = {'data': self._data} for ref, (m, _) in self._models.copy().items(): self._apply_update(named_events, msg, m.source, ref) def _process_param_change(self, params): if 'disabled' in params: params['editable'] = not params.pop('disabled') and len(self.indexes) <= 1 params = super()._process_param_change(params) return params def _get_properties(self, doc: Document | None = None) -> dict[str, Any]: properties = super()._get_properties(doc) properties['columns'] = self._get_columns() properties['source'] = cds = ColumnDataSource(data=self._data) cds.selected.indices = self.selection return properties def _get_model( self, doc: Document, root: Model | None = None, parent: Model | None = None, comm: Comm | None = None ) -> Model: properties = self._get_properties(doc) model = self._widget_type(**properties) # type: ignore root = root or model self._link_props(model.source, ['data'], doc, root, comm) self._link_props(model.source.selected, ['indices'], doc, root, comm) self._models[root.ref['id']] = (model, parent) return model def _update_columns(self, event: param.parameterized.Event, model: Model): if event.name == 'value' and [c.field for c in model.columns] == self._get_fields(): # Skip column update if the data has changed but the columns # have not return model.columns = self._get_columns() def _manual_update( self, events: tuple[param.parameterized.Event, ...], model: Model, doc: Document, root: Model, parent: Model | None, comm: Comm | None ) -> None: for event in events: if event.type == 'triggered' and self._updating: continue elif event.name in ('value', 'show_index'): self._update_columns(event, model) if isinstance(model, DataCube): model.groupings = self._get_groupings() elif hasattr(self, '_update_' + event.name): getattr(self, '_update_' + event.name)(model) else: self._update_columns(event, model) def _sort_df(self, df: pd.DataFrame) -> pd.DataFrame: if not self.sorters: return df fields = [self._renamed_cols.get(s['field'], s['field']) for s in self.sorters] ascending = [s['dir'] == 'asc' for s in self.sorters] # Making a copy of the DataFrame because it could be a view of the original # dataframe. There could be a better place to do this. df = df.copy() # Temporarily add _index_ column because Tabulator uses internal _index # as additional sorter to break ties df['_index_'] = np.arange(len(df)).astype(str) fields.append('_index_') ascending.append(True) # Handle sort on index column if show_index=True if self.show_index: rename = 'index' in fields and df.index.name is None if rename: df.index.name = 'index' else: rename = False def tabulator_sorter(col): # Tabulator JS defines its own sorting algorithm: # - strings's case isn't taken into account if col.dtype.kind not in 'SUO': return col try: return col.fillna("").str.lower() except Exception: return col df_sorted = df.sort_values(fields, ascending=ascending, kind='mergesort', key=tabulator_sorter) # Revert temporary changes to DataFrames if rename: df_sorted.index.name = None df_sorted.drop(columns=['_index_'], inplace=True) return df_sorted def _filter_dataframe( self, df: pd.DataFrame, header_filters: bool = True, internal_filters: bool = True ) -> pd.DataFrame: """ Filter the DataFrame. Parameters ---------- df : DataFrame The DataFrame to filter Returns ------- DataFrame The filtered DataFrame """ filters = [] for col_name, filt in (self._filters if internal_filters else []): if col_name is not None and col_name not in df.columns: continue if isinstance(filt, (FunctionType, MethodType, partial)): res = filt(df) if type(res) is type(df): #function returned filtered dataframe df = res else: #assume boolean mask filters.append(res) continue if isinstance(filt, param.Parameter): val = getattr(filt.owner, filt.name) else: val = filt column = df[col_name] if val is None: continue elif np.isscalar(val): mask = column == val elif isinstance(val, (list, set)): if not val: continue mask = column.isin(val) elif isinstance(val, tuple): start, end = val if start is None and end is None: continue elif start is None: mask = column<=end elif end is None: mask = column>=start else: mask = (column>=start) & (column<=end) else: raise ValueError(f"'{col_name} filter value not " "understood. Must be either a scalar, " "tuple or list.") filters.append(mask) if header_filters: filters.extend(self._get_header_filters(df)) if filters: mask = filters[0] for f in filters: mask &= f if self._edited_indexes: edited_mask = (df.index.isin(self._edited_indexes)) mask = mask | edited_mask df = df[mask] return df def _get_header_filters(self, df: pd.DataFrame) -> list[pd.Series | np.ndarray]: filters = [] for filt in getattr(self, 'filters', []): col_name = filt['field'] op = filt['type'] val = filt['value'] filt_def = getattr(self, 'header_filters', {}) or {} if col_name in df.columns: col = df[col_name] elif col_name in self.indexes: if len(self.indexes) == 1: col = df.index else: col = df.index.get_level_values(self.indexes.index(col_name)) else: continue # Sometimes Tabulator will provide a zero/single element list if isinstance(val, list): if len(val) == 1: val = val[0] elif not val: continue if col.dtype.kind != 'O': val = col.dtype.type(val) if op == '=': filters.append(col == val) elif op == '!=': filters.append(col != val) elif op == '<': filters.append(col < val) elif op == '>': filters.append(col > val) elif op == '>=': filters.append(col >= val) elif op == '<=': filters.append(col <= val) elif op == 'in': if not isinstance(val, (list, np.ndarray)): val = [val] filters.append(col.isin(val)) elif op == 'like': filters.append(col.str.contains(val, case=False, regex=False)) elif op == 'starts': filters.append(col.str.startsWith(val)) elif op == 'ends': filters.append(col.str.endsWith(val)) elif op == 'keywords': match_all = filt_def.get(col_name, {}).get('matchAll', False) sep = filt_def.get(col_name, {}).get('separator', ' ') matches = val.split(sep) if match_all: for match in matches: filters.append(col.str.contains(match, case=False, regex=False)) else: filt = col.str.contains(matches[0], case=False, regex=False) for match in matches[1:]: filt |= col.str.contains(match, case=False, regex=False) filters.append(filt) elif op == 'regex': raise ValueError("Regex filtering not supported.") else: raise ValueError(f"Filter type {op!r} not recognized.") return filters def add_filter(self, filter: Any, column: str | None = None): """ Adds a filter to the table which can be a static value or dynamic parameter based object which will automatically update the table when changed.. When a static value, widget or parameter is supplied the filtering will follow a few well defined behaviors: * scalar: Filters by checking for equality * tuple: A tuple will be interpreted as range. * list: A list will be interpreted as a set of discrete scalars and the filter will check if the values in the column match any of the items in the list. Parameters ---------- filter: Widget, param.Parameter or FunctionType The value by which to filter the DataFrame along the declared column, or a function accepting the DataFrame to be filtered and returning a filtered copy of the DataFrame. column: str or None Column to which the filter will be applied, if the filter is a constant value, widget or parameter. Raises ------ ValueError: If the filter type is not supported or no column was declared. """ if isinstance(filter, (tuple, list, set)) or np.isscalar(filter): deps = [] elif isinstance(filter, (FunctionType, MethodType, partial)): deps = list(filter._dinfo['kw'].values()) if hasattr(filter, '_dinfo') else [] else: filter = transform_reference(filter) if not isinstance(filter, param.Parameter): raise ValueError(f'{type(self).__name__} filter must be ' 'a constant value, parameter, widget ' 'or function.') elif column is None: raise ValueError('When filtering with a parameter or ' 'widget, a column to filter on must be ' 'declared.') deps = [filter] for dep in deps: dep.owner.param.watch(self._update_cds, dep.name) self._filters.append((column, filter)) self._update_cds() def remove_filter(self, filter): """ Removes a filter which was previously added. """ self._filters = [(column, filt) for (column, filt) in self._filters if filt is not filter] self._update_cds() def _process_column(self, values: TDataColumn): if not isinstance(values, (list, np.ndarray)): return [str(v) for v in values] if isinstance(values, np.ndarray) and values.dtype.kind == "b": # Workaround for https://github.com/bokeh/bokeh/issues/12776 return values.tolist() return values def _get_data(self) -> tuple[pd.DataFrame, DataDict]: return self._process_df_and_convert_to_cds(self.value) def _process_df_and_convert_to_cds(self, df: pd.DataFrame) -> tuple[pd.DataFrame, DataDict]: # By default we potentially have two distinct views of the data # locally we hold the fully filtered data, i.e. with header filters # applied but since header filters are applied on the frontend # we send the unfiltered data import pandas as pd df = self._filter_dataframe(df, header_filters=False) if df is None: return [], {} if isinstance(self.value.index, pd.MultiIndex): indexes = [ f'level_{i}' if n is None else n for i, n in enumerate(df.index.names) ] else: default_index = ('level_0' if 'index' in df.columns else 'index') indexes = [df.index.name or default_index] data = ColumnDataSource.from_df(df.reset_index() if len(indexes) > 1 else df) if not self.show_index and len(indexes) > 1: data = {k: v for k, v in data.items() if k not in indexes} return df, {k if isinstance(k, str) else str(k): self._process_column(v) for k, v in data.items()} def _update_column(self, column: str, array: TDataColumn): import pandas as pd self.value[column] = array if self._processed is not None and self.value is not self._processed: with pd.option_context('mode.chained_assignment', None): self._processed[column] = array #---------------------------------------------------------------- # Public API #---------------------------------------------------------------- @property def indexes(self): import pandas as pd if self.value is None or not self.show_index: return [] elif isinstance(self.value.index, pd.MultiIndex): return [ f'level_{i}' if n is None else n for i, n in enumerate(self.value.index.names) ] default_index = ('level_0' if 'index' in self.value.columns else 'index') return [self.value.index.name or default_index] def stream(self, stream_value, rollover=None, reset_index=True): """ Streams (appends) the `stream_value` provided to the existing value in an efficient manner. Parameters ---------- stream_value: (pd.DataFrame | pd.Series | Dict) The new value(s) to append to the existing value. rollover: int A maximum column size, above which data from the start of the column begins to be discarded. If None, then columns will continue to grow unbounded. reset_index: (bool, default=True) If True and the stream_value is a DataFrame, then its index is reset. Helps to keep the index unique and named `index` Raises ------ ValueError: Raised if the stream_value is not a supported type. Examples -------- Stream a Series to a DataFrame >>> value = pd.DataFrame({"x": [1, 2], "y": ["a", "b"]}) >>> tabulator = Tabulator(value=value) >>> stream_value = pd.Series({"x": 4, "y": "d"}) >>> tabulator.stream(stream_value) >>> tabulator.value.to_dict("list") {'x': [1, 2, 4], 'y': ['a', 'b', 'd']} Stream a Dataframe to a Dataframe >>> value = pd.DataFrame({"x": [1, 2], "y": ["a", "b"]}) >>> tabulator = Tabulator(value=value) >>> stream_value = pd.DataFrame({"x": [3, 4], "y": ["c", "d"]}) >>> tabulator.stream(stream_value) >>> tabulator.value.to_dict("list") {'x': [1, 2, 3, 4], 'y': ['a', 'b', 'c', 'd']} Stream a Dictionary row to a DataFrame >>> value = pd.DataFrame({"x": [1, 2], "y": ["a", "b"]}) >>> tabulator = Tabulator(value=value) >>> stream_value = {"x": 4, "y": "d"} >>> tabulator.stream(stream_value) >>> tabulator.value.to_dict("list") {'x': [1, 2, 4], 'y': ['a', 'b', 'd']} Stream a Dictionary of Columns to a Dataframe >>> value = pd.DataFrame({"x": [1, 2], "y": ["a", "b"]}) >>> tabulator = Tabulator(value=value) >>> stream_value = {"x": [3, 4], "y": ["c", "d"]} >>> tabulator.stream(stream_value) >>> tabulator.value.to_dict("list") {'x': [1, 2, 3, 4], 'y': ['a', 'b', 'c', 'd']} """ import pandas as pd if not np.isfinite(self.value.index.max()): value_index_start = 1 else: value_index_start = self.value.index.max() + 1 if isinstance(stream_value, pd.DataFrame): if reset_index: stream_value = stream_value.reset_index(drop=True) stream_value.index += value_index_start if self.value.empty: combined = pd.DataFrame( stream_value, columns=self.value.columns ).astype(self.value.dtypes) else: combined = pd.concat([self.value, stream_value]) if rollover is not None: combined = combined.iloc[-rollover:] with param.discard_events(self): self.value = combined try: self._updating = True self.param.trigger('value') finally: self._updating = False stream_value, stream_data = self._process_df_and_convert_to_cds(stream_value) try: self._updating = True self._stream(stream_data, rollover) finally: self._updating = False elif isinstance(stream_value, pd.Series): self.value.loc[value_index_start] = stream_value if rollover is not None and len(self.value) > rollover: with param.discard_events(self): self.value = self.value.iloc[-rollover:] stream_value, stream_data = self._process_df_and_convert_to_cds(self.value.iloc[-1:]) try: self._updating = True self._stream(stream_data, rollover) finally: self._updating = False elif isinstance(stream_value, dict): if stream_value: try: stream_value = pd.DataFrame(stream_value) except ValueError: stream_value = pd.Series(stream_value) self.stream(stream_value, rollover) else: raise ValueError("The stream value provided is not a DataFrame, Series or Dict!") def patch(self, patch_value, as_index=True): """ Efficiently patches (updates) the existing value with the `patch_value`. Parameters ---------- patch_value: (pd.DataFrame | pd.Series | Dict) The value(s) to patch the existing value with. as_index: boolean Whether to treat the patch index as DataFrame indexes (True) or as simple integer index. Raises ------ ValueError: Raised if the patch_value is not a supported type. Examples -------- Patch a DataFrame with a Dictionary row. >>> value = pd.DataFrame({"x": [1, 2], "y": ["a", "b"]}) >>> tabulator = Tabulator(value=value) >>> patch_value = {"x": [(0, 3)]} >>> tabulator.patch(patch_value) >>> tabulator.value.to_dict("list") {'x': [3, 2], 'y': ['a', 'b']} Patch a Dataframe with a Dictionary of Columns. >>> value = pd.DataFrame({"x": [1, 2], "y": ["a", "b"]}) >>> tabulator = Tabulator(value=value) >>> patch_value = {"x": [(slice(2), (3,4))], "y": [(1,'d')]} >>> tabulator.patch(patch_value) >>> tabulator.value.to_dict("list") {'x': [3, 4], 'y': ['a', 'd']} Patch a DataFrame with a Series. Please note the index is used in the update. >>> value = pd.DataFrame({"x": [1, 2], "y": ["a", "b"]}) >>> tabulator = Tabulator(value=value) >>> patch_value = pd.Series({"index": 1, "x": 4, "y": "d"}) >>> tabulator.patch(patch_value) >>> tabulator.value.to_dict("list") {'x': [1, 4], 'y': ['a', 'd']} Patch a Dataframe with a Dataframe. Please note the index is used in the update. >>> value = pd.DataFrame({"x": [1, 2], "y": ["a", "b"]}) >>> tabulator = Tabulator(value=value) >>> patch_value = pd.DataFrame({"x": [3, 4], "y": ["c", "d"]}) >>> tabulator.patch(patch_value) >>> tabulator.value.to_dict("list") {'x': [3, 4], 'y': ['c', 'd']} """ if self.value is None: raise ValueError(f"Cannot patch empty {type(self).__name__}.") import pandas as pd if not isinstance(self.value, pd.DataFrame): raise ValueError( f"Patching an object of type {type(self.value).__name__} " "is not supported. Please provide a dict." ) if isinstance(patch_value, pd.DataFrame): patch_value_dict = { column: list(patch_value[column].items()) for column in patch_value.columns } self.patch(patch_value_dict, as_index=as_index) elif isinstance(patch_value, pd.Series): if "index" in patch_value: # Series orient is row patch_value_dict = { k: [(patch_value["index"], v)] for k, v in patch_value.items() } patch_value_dict.pop("index") else: # Series orient is column patch_value_dict = {patch_value.name: list(patch_value.items())} self.patch(patch_value_dict, as_index=as_index) elif isinstance(patch_value, dict): columns = list(self.value.columns) patches = {} for k, v in patch_value.items(): values = [] for (patch_ind, value) in v: data_ind = patch_ind if isinstance(patch_ind, slice): data_ind = range(patch_ind.start, patch_ind.stop, patch_ind.step or 1) if as_index: if not isinstance(data_ind, range): patch_ind = self.value.index.get_loc(patch_ind) if not isinstance(patch_ind, int): raise ValueError( 'Patching a table with duplicate index values is not supported. ' f'Found this duplicate index: {data_ind!r}' ) self.value.loc[data_ind, k] = value else: self.value.iloc[data_ind, columns.index(k)] = value if isinstance(value, pd.Timestamp): value = datetime_as_utctimestamp(value) elif value is pd.NaT: value = np.nan values.append((patch_ind, value)) patches[k] = values self._patch(patches) else: raise ValueError( f"Patching with a patch_value of type {type(patch_value).__name__} " "is not supported. Please provide a DataFrame, Series or Dict." ) @property def current_view(self): """ Returns the current view of the table after filtering and sorting are applied. """ df = self._processed return self._sort_df(df) @property def selected_dataframe(self): """ Returns a DataFrame of the currently selected rows. """ if not self.selection: return self.current_view.iloc[:0] df = self.value.iloc[self.selection] return self._filter_dataframe(df) class DataFrame(BaseTable): """ The `DataFrame` widget allows displaying and editing a pandas DataFrame. Note that editing is not possible for multi-indexed DataFrames, in which case you will need to reduce the DataFrame to a single index. Also note that the `DataFrame` widget will eventually be replaced with the `Tabulator` widget, and so new code should be written to use `Tabulator` instead. Reference: https://panel.holoviz.org/reference/widgets/DataFrame.html :Example: >>> DataFrame(df, name='DataFrame') """ auto_edit = param.Boolean(default=False, doc=""" Whether clicking on a table cell automatically starts edit mode.""") autosize_mode = param.Selector(default='force_fit', objects=[ "none", "fit_columns", "fit_viewport", "force_fit"], doc=""" Determines the column autosizing mode, as one of the following options: ``"fit_columns"`` Compute column widths based on cell contents while ensuring the table fits into the available viewport. This results in no horizontal scrollbar showing up, but data can get unreadable if there is not enough space available. ``"fit_viewport"`` Adjust the viewport size after computing column widths based on cell contents. ``"force_fit"`` Fit columns into available space dividing the table width across the columns equally (equivalent to `fit_columns=True`). This results in no horizontal scrollbar showing up, but data can get unreadable if there is not enough space available. ``"none"`` Do not automatically compute column widths.""") fit_columns = param.Boolean(default=None, doc=""" Whether columns should expand to the available width. This results in no horizontal scrollbar showing up, but data can get unreadable if there is no enough space available.""") frozen_columns = param.Integer(default=None, doc=""" Integer indicating the number of columns to freeze. If set, the first N columns will be frozen, which prevents them from scrolling out of frame.""") frozen_rows = param.Integer(default=None, doc=""" Integer indicating the number of rows to freeze. If set, the first N rows will be frozen, which prevents them from scrolling out of frame; if set to a negative value the last N rows will be frozen.""") reorderable = param.Boolean(default=True, doc=""" Allows the reordering of a table's columns. To reorder a column, click and drag a table's header to the desired location in the table. The columns on either side will remain in their previous order.""") sortable = param.Boolean(default=True, doc=""" Allows to sort table's contents. By default natural order is preserved. To sort a column, click on its header. Clicking one more time changes sort direction. Use Ctrl + click to return to natural order. Use Shift + click to sort multiple columns simultaneously.""") _manual_params: ClassVar[list[str]] = BaseTable._manual_params + ['aggregators'] _aggregators = { 'sum': SumAggregator, 'max': MaxAggregator, 'min': MinAggregator, 'mean': AvgAggregator } _source_transforms: ClassVar[Mapping[str, str | None]] = {'hierarchical': None} _rename: ClassVar[Mapping[str, str | None]] = { 'selection': None, 'sorters': None, 'text_align': None } @property def _widget_type(self) -> type[Model] | None: # type: ignore[override] return DataCube if self.hierarchical else DataTable def _get_columns(self): if self.value is None: return [] indexes = self.indexes col_names = list(self.value.columns) if not self.hierarchical or len(indexes) == 1: col_names = indexes + col_names else: col_names = indexes[-1:] + col_names df = self.value.reset_index() if len(indexes) > 1 else self.value return self._get_column_definitions(col_names, df) def _get_groupings(self): if self.value is None: return [] groups = [] for group, agg_group in zip(self.indexes[:-1], self.indexes[1:]): if str(group) != group: self._renamed_cols[str(group)] = group aggs = self._get_aggregators(agg_group) groups.append(GroupingInfo(getter=str(group), aggregators=aggs)) return groups def _get_aggregators(self, group): numeric_cols = list(self.value.select_dtypes(include='number').columns) aggs = self.aggregators.get(group, []) if not isinstance(aggs, list): aggs = [aggs] expanded_aggs = [] for col_aggs in aggs: if not isinstance(col_aggs, dict): col_aggs = {col: col_aggs for col in numeric_cols} for col, agg in col_aggs.items(): if isinstance(agg, str): agg = self._aggregators.get(agg) if issubclass(agg, RowAggregator): expanded_aggs.append(agg(field_=str(col))) return expanded_aggs def _get_properties(self, doc: Document | None = None) -> dict[str, Any]: properties = super()._get_properties(doc) if self.hierarchical: properties['target'] = ColumnDataSource(data=dict(row_indices=[], labels=[])) properties['grouping'] = self._get_groupings() return properties def _update_aggregators(self, model): for g in model.grouping: group = self._renamed_cols.get(g.getter, g.getter) index = self.indexes[self.indexes.index(group)+1] g.aggregators = self._get_aggregators(index) class _ListValidateWithCallable(param.List): __slots__ = ['callable'] def __init__(self, **params): self.callable = params.pop("callable", None) super().__init__(**params) def _validate(self, val): super()._validate(val) self._validate_callable(val) def _validate_callable(self, val): if self.callable is not None: selectable = self.callable() if selectable and val: if set(val) - set(selectable): raise ValueError( "Values in 'selection' must not have values " "which are not available with 'selectable_rows'." ) class Tabulator(BaseTable): """ The `Tabulator` widget wraps the [Tabulator js](http://tabulator.info/) table to provide a full-featured, very powerful interactive table. Reference: https://panel.holoviz.org/reference/widgets/Tabulator.html :Example: >>> Tabulator(df, theme='site', pagination='remote', page_size=25) """ buttons = param.Dict(default={}, nested_refs=True, doc=""" Dictionary mapping from column name to a HTML element to use as the button icon.""") expanded = param.List(default=[], nested_refs=True, doc=""" List of expanded rows, only applicable if a row_content function has been defined.""") embed_content = param.Boolean(default=False, doc=""" Whether to embed the row_content or render it dynamically when a row is expanded.""") filters = param.List(default=[], doc=""" List of client-side filters declared as dictionaries containing 'field', 'type' and 'value' keys.""") frozen_columns = param.ClassSelector(class_=(list, dict), default=[], nested_refs=True, doc=""" One of: - List indicating the columns to freeze. The column(s) may be selected by name or index. - Dict indicating columns to freeze as keys and their freeze location as values, freeze location is either 'right' or 'left'.""") frozen_rows = param.List(default=[], nested_refs=True, doc=""" List indicating the rows to freeze. If set, the first N rows will be frozen, which prevents them from scrolling out of frame; if set to a negative value the last N rows will be frozen.""") groups = param.Dict(default={}, nested_refs=True, doc=""" Dictionary mapping defining the groups.""") groupby = param.List(default=[], nested_refs=True, doc=""" Groups rows in the table by one or more columns.""") header_align = param.ClassSelector(default={}, nested_refs=True, class_=(dict, str), doc=""" A mapping from column name to alignment or a fixed column alignment, which should be one of 'left', 'center', 'right'.""") header_filters = param.ClassSelector(class_=(bool, dict), nested_refs=True, doc=""" Whether to enable filters in the header or dictionary configuring filters for each column.""") header_tooltips = param.Dict(default={}, doc=""" Dictionary mapping from column name to a tooltip to show when hovering over the column header.""") hidden_columns = param.List(default=[], nested_refs=True, doc=""" List of columns to hide.""") layout = param.Selector(default='fit_data_table', objects=[ 'fit_data', 'fit_data_fill', 'fit_data_stretch', 'fit_data_table', 'fit_columns']) initial_page_size = param.Integer(default=20, bounds=(1, None), doc=""" Initial page size if page_size is None and therefore automatically set.""") pagination = param.Selector(default=None, allow_None=True, objects=['local', 'remote']) page = param.Integer(default=1, doc=""" Currently selected page (indexed starting at 1), if pagination is enabled.""") page_size = param.Integer(default=None, bounds=(1, None), doc=""" Number of rows to render per page, if pagination is enabled.""") row_content = param.Callable(doc=""" A function which is given the DataFrame row and should return a Panel object to render as additional detail below the row.""") row_height = param.Integer(default=30, doc=""" The height of each table row.""") selection: list[int] = _ListValidateWithCallable(default=[], doc=""" The currently selected rows of the table. It validates its values against 'selectable_rows' if used.""") selectable = param.ClassSelector( default=True, class_=(bool, str, int), doc=""" Defines the selection mode of the Tabulator. - True Selects rows on click. To select multiple use Ctrl-select, to select a range use Shift-select - False Disables selection - 'checkbox' Adds a column of checkboxes to toggle selections - 'checkbox-single' Same as 'checkbox' but header does not allow select/deselect all - 'toggle' Selection toggles when clicked - int The maximum number of selectable rows. """) selectable_rows = param.Callable(default=None, doc=""" A function which given a DataFrame should return a list of rows by integer index, which are selectable.""") sortable = param.ClassSelector(default=True, class_=(bool, dict), doc=""" Whether the columns in the table should be sortable. Can either be specified as a simple boolean toggling the behavior on and off or as a dictionary specifying the option per column.""") theme = param.Selector( default="simple", objects=[ 'default', 'site', 'simple', 'midnight', 'modern', 'bootstrap', 'bootstrap4', 'materialize', 'bulma', 'semantic-ui', 'fast', 'bootstrap5' ], doc=""" Tabulator CSS theme to apply to table.""") theme_classes = param.List(default=[], nested_refs=True, item_type=str, doc=""" List of extra CSS classes to apply to the Tabulator element to customize the theme.""") title_formatters = param.Dict(default={}, nested_refs=True, doc=""" Tabulator formatter specification to use for a particular column header title.""") _data_params: ClassVar[list[str]] = [ 'value', 'page', 'page_size', 'pagination', 'sorters', 'filters' ] _config_params: ClassVar[list[str]] = [ 'frozen_columns', 'groups', 'selectable', 'hierarchical', 'sortable' ] _content_params: ClassVar[list[str]] = _data_params + ['expanded', 'row_content', 'embed_content'] _manual_params: ClassVar[list[str]] = BaseTable._manual_params + _config_params _priority_changes: ClassVar[list[str]] = ['data', 'filters'] _rename: ClassVar[Mapping[str, str | None]] = { 'selection': None, 'row_content': None, 'row_height': None, 'text_align': None, 'header_align': None, 'header_filters': None, 'header_tooltips': None, 'styles': 'cell_styles', 'title_formatters': None, 'sortable': None, 'initial_page_size': None } # Determines the maximum size limits beyond which (local, remote) # pagination is enabled _MAX_ROW_LIMITS: ClassVar[tuple[int, int]] = (200, 10000) _stylesheets = [CSS_URLS['font-awesome']] def __init__(self, value=None, **params): import pandas.io.formats.style click_handler = params.pop('on_click', None) edit_handler = params.pop('on_edit', None) if isinstance(value, pandas.io.formats.style.Styler): style = value value = value.data else: style = None configuration = params.pop('configuration', {}) self.style = None self._computed_styler = None self._child_panels = {} self._indexed_children = {} self._explicit_pagination = 'pagination' in params self._on_edit_callbacks = [] self._on_click_callbacks = {} self._old_value = None super().__init__(value=value, **params) self._configuration = configuration self.param.watch(self._update_children, self._content_params) self.param.watch(self._clear_selection_remote_pagination, 'value') if click_handler: self.on_click(click_handler) if edit_handler: self.on_edit(edit_handler) if style is not None: self.style._todo = style._todo self.param.selection.callable = self._get_selectable @param.depends('value', watch=True, on_init=True) def _apply_max_size(self): """ Ensure large tables automatically enable remote pagination. """ if self.value is None or self._explicit_pagination: return with param.parameterized.discard_events(self): if self.hierarchical: pass elif self._MAX_ROW_LIMITS[0] < len(self.value) <= self._MAX_ROW_LIMITS[1]: self.pagination = 'local' elif len(self.value) > self._MAX_ROW_LIMITS[1]: self.pagination = 'remote' self._explicit_pagination = False @param.depends('pagination', watch=True) def _set_explicict_pagination(self): self._explicit_pagination = True @staticmethod def _validate_iloc(idx, iloc): # Validate that the index returned by Pandas get_loc is a single int, # as get_loc can return a slice or a mask array when it finds more # than one locations. if not isinstance(iloc, int): raise ValueError( 'The Tabulator widget expects the provided `value` Pandas DataFrame ' 'to have unique indexes, in particular when it has to deal with ' f'click or edit events. Found this duplicate index: {idx!r}' ) def _validate(self, *events): super()._validate(*events) if self.value is not None: todo = [] if self.style is not None: todo = self.style._todo try: self.style = self.value.style self.style._todo = todo except Exception: pass def _cleanup(self, root: Model | None = None) -> None: for p in self._child_panels.values(): p._cleanup(root) super()._cleanup(root) def _process_events(self, events: dict[str, Any]) -> None: if 'expanded' in events: self._update_expanded(events.pop('expanded')) if events.get('page_size') == 0: # page_size can't be 0 events.pop('page_size') return super()._process_events(events) def _process_event(self, event) -> None: if event.event_name == 'selection-change': if self.pagination == 'remote': self._update_selection(event) return event_col = self._renamed_cols.get(event.column, event.column) if self.pagination == 'remote': nrows = self.page_size or self.initial_page_size event.row = event.row+(self.page-1)*nrows idx = self._index_mapping.get(event.row, event.row) iloc = self.value.index.get_loc(idx) self._validate_iloc(idx, iloc) event.row = iloc if event_col not in self.buttons: if event_col in self.value.columns: event.value = self.value[event_col].iloc[event.row] else: event.value = self.value.index[event.row] # Set the old attribute on a table edit event if event.event_name == 'table-edit': if event.pre: import pandas as pd filter_df = pd.DataFrame({event.column: [event.value]}) filters = self._get_header_filters(filter_df) # Check if edited cell was filtered if filters and filters[0].any(): self._edited_indexes.append(idx) else: if self._old_value is not None: event.old = self._old_value[event_col].iloc[event.row] for cb in self._on_edit_callbacks: state.execute(partial(cb, event), schedule=False) self._update_style() else: for cb in self._on_click_callbacks.get(None, []): state.execute(partial(cb, event), schedule=False) for cb in self._on_click_callbacks.get(event_col, []): state.execute(partial(cb, event), schedule=False) def _get_theme(self, theme, resources=None): from ..models.tabulator import _TABULATOR_THEMES_MAPPING, THEME_PATH theme_ = _TABULATOR_THEMES_MAPPING.get(theme, theme) fname = 'tabulator' if theme_ == 'default' else f'tabulator_{theme_}' theme_url = f'{CDN_DIST}bundled/datatabulator/{THEME_PATH}{fname}.min.css' if self._widget_type is not None: self._widget_type.__css_raw__ = [theme_url] return theme_url def _update_columns(self, event, model): if event.name not in self._config_params: super()._update_columns(event, model) if (event.name in ('editors', 'formatters', 'sortable') and not any(isinstance(v, (str, dict)) for v in event.new.values())): # If no tabulator editor/formatter was changed we can skip # update to config return model.configuration = self._get_configuration(model.columns) def _process_data(self, data): # Extending _process_data to cover the case when header filters are # active and a cell is edited. In that case the data received from the # front-end is the full table, not just the filtered one. However the # _processed data is already filtered, this made the comparison between # the new data and old data wrong. This extension replicates the # front-end filtering - if need be - to be able to correctly make the # comparison and update the data held by the backend. # It also makes a copy of the value dataframe, to use it to obtain # the old value in a table-edit event. self._old_value = self.value.copy() import pandas as pd df = pd.DataFrame(data) filters = self._get_header_filters(df) if self.pagination == 'remote' else [] if filters: mask = filters[0] for f in filters: mask &= f if self._edited_indexes: edited_mask = (df[self.value.index.name or 'index'].isin(self._edited_indexes)) mask = mask | edited_mask df = df[mask] data = { col: df[col].values for col in df.columns } return super()._process_data(data) def _get_data(self): if self.pagination != 'remote' or self.value is None: return super()._get_data() # If data is paginated the current view on the frontend # and locally are identical and both paginated import pandas as pd df = self._filter_dataframe(self.value) df = self._sort_df(df) nrows = self.page_size or self.initial_page_size start = (self.page-1)*nrows page_df = df.iloc[start: start+nrows] if isinstance(self.value.index, pd.MultiIndex): indexes = [ f'level_{i}' if n is None else n for i, n in enumerate(df.index.names) ] else: default_index = ('level_0' if 'index' in df.columns else 'index') indexes = [df.index.name or default_index] if len(indexes) > 1: page_df = page_df.reset_index() data = ColumnDataSource.from_df(page_df).items() return df, {k if isinstance(k, str) else str(k): v for k, v in data} def _get_style_data(self, recompute=True): if self.value is None or self.style is None or self.value.empty: return {} df = self._processed if len(self.indexes) > 1: df = df.reset_index() if recompute: try: self._computed_styler = styler = df.style except Exception: self._computed_styler = None return {} if styler is None: return {} styler._todo = styler_update(self.style, df) try: styler._compute() except Exception: styler._todo = [] else: styler = self._computed_styler if styler is None: return {} # Compute offsets (not that multi-indexes are reset so don't require an offset) offset = 1 + int(len(self.indexes) == 1) + int(self.selectable in ('checkbox', 'checkbox-single')) + int(bool(self.row_content)) if self.pagination == 'remote': page_size = self.page_size or self.initial_page_size start = (self.page - 1) * page_size end = start + page_size # Map column indexes in the data to indexes after frozen_columns are applied column_mapper = {} frozen_cols = self.frozen_columns column_mapper = {} if isinstance(frozen_cols, list): nfrozen = len(frozen_cols) non_frozen = [col for col in df.columns if col not in frozen_cols] for i, col in enumerate(df.columns): if col in frozen_cols: column_mapper[i] = frozen_cols.index(col) - len(self.indexes) else: column_mapper[i] = nfrozen + non_frozen.index(col) elif isinstance(frozen_cols, dict): left_cols = [col for col, p in frozen_cols.items() if p in 'left'] right_cols = [col for col, p in frozen_cols.items() if p in 'right'] non_frozen = [col for col in df.columns if col not in frozen_cols] for i, col in enumerate(df.columns): if col in left_cols: column_mapper[i] = left_cols.index(col) - len(self.indexes) elif col in right_cols: column_mapper[i] = len(left_cols) + len(non_frozen) + right_cols.index(col) else: column_mapper[i] = len(left_cols) + non_frozen.index(col) styles = {} for (r, c), s in styler.ctx.items(): if self.pagination == 'remote': if (r < start or r >= end): continue else: r -= start if r not in styles: styles[int(r)] = {} c = column_mapper.get(int(c), int(c)) styles[int(r)][offset+c] = s return {'id': uuid.uuid4().hex, 'data': styles} def _get_selectable(self): if self.value is None or self.selectable_rows is None: return None df = self._processed if self.pagination == 'remote': nrows = self.page_size or self.initial_page_size start = (self.page-1)*nrows df = df.iloc[start:(start+nrows)] return self.selectable_rows(df) def _update_style(self, recompute=True): styles = self._get_style_data(recompute) msg = {'cell_styles': styles} for ref, (m, _) in self._models.copy().items(): self._apply_update([], msg, m, ref) def _get_children(self): if self.row_content is None or self.value is None: return {}, [], [] from ..pane import panel df = self._processed if self.pagination == 'remote': nrows = self.page_size or self.initial_page_size start = (self.page-1)*nrows df = df.iloc[start:(start+nrows)] indexed_children, children = {}, {} if self.embed_content: indexes = list(range(len(df))) mapped = self._map_indexes(indexes) expanded = [ i for i, m in zip(indexes, mapped) if m in self.expanded ] for i in indexes: idx = df.index[i] if idx in self._indexed_children: child = self._indexed_children[idx] else: child = panel(self.row_content(df.iloc[i])) indexed_children[idx] = children[i] = child else: expanded = [] for i in self.expanded: idx = self.value.index[i] if idx in self._indexed_children: child = self._indexed_children[idx] else: child = panel(self.row_content(self.value.iloc[i])) try: loc = df.index.get_loc(idx) except KeyError: continue expanded.append(loc) indexed_children[idx] = children[loc] = child removed = [ child for idx, child in self._indexed_children.items() if idx not in indexed_children ] self._indexed_children = indexed_children return children, removed, expanded def _get_model_children(self, doc, root, parent, comm=None): ref = root.ref['id'] models = {} for i, p in self._child_panels.items(): if ref in p._models: model = p._models[ref][0] else: model = p._get_model(doc, root, parent, comm) model.margin = (0, 0, 0, 0) models[i] = model return models def _update_children(self, *events): page_event = all(e.name in ('page', 'page_size', 'pagination', 'sorters') for e in events) if (page_event and self.pagination != 'remote'): return for event in events: if event.name == 'value' and self._indexes_changed(event.old, event.new): self.expanded = [] self._indexed_children.clear() return elif event.name == 'row_content': self._indexed_children.clear() self._child_panels, removed, expanded = self._get_children() for ref, (m, _) in self._models.copy().items(): msg = {'expanded': expanded} if not self.embed_content or any(e.name == 'row_content' for e in events): root, doc, comm = state._views[ref][1:] for child_panel in removed: child_panel._cleanup(root) msg['children'] = self._get_model_children(doc, root, m, comm) self._apply_update([], msg, m, ref) @updating def _stream(self, stream, rollover=None, follow=True): if self.pagination == 'remote': length = self._length nrows = self.page_size or self.initial_page_size max_page = max(length//nrows + bool(length%nrows), 1) if self.page != max_page and not follow: return self._processed, _ = self._get_data() return super()._stream(stream, rollover) self._update_style() self._update_selectable() self._update_index_mapping() def stream(self, stream_value, rollover=None, reset_index=True, follow=True): for ref, (model, _) in self._models.copy().items(): self._apply_update([], {'follow': follow}, model, ref) super().stream(stream_value, rollover, reset_index) if follow and self.pagination: self._update_max_page() if follow and self.pagination: length = self._length nrows = self.page_size or self.initial_page_size self.page = max(length//nrows + bool(length%nrows), 1) @updating def _patch(self, patch): if self.filters or self._filters or self.sorters: self._updating = False self._update_cds() return if self.pagination == 'remote': nrows = self.page_size or self.initial_page_size start = (self.page - 1) * nrows end = start+nrows filtered = {} for c, values in patch.items(): values = [(ind, val) for (ind, val) in values if ind >= start and ind < end] if values: filtered[c] = values patch = filtered if not patch: return super()._patch(patch) self._update_style() self._update_selectable() def _update_cds(self, *events): if any(event.name == 'filters' for event in events): self._edited_indexes = [] page_events = ('page', 'page_size', 'sorters') if self._updating: return elif events and all(e.name in page_events for e in events) and self.pagination == 'local': return elif events and all(e.name in page_events for e in events) and not self.pagination: self._processed, _ = self._get_data() return elif self.pagination == 'remote': self._processed = None recompute = not all( e.name in ('page', 'page_size', 'pagination') for e in events ) super()._update_cds(*events) if self.pagination: self._update_max_page() self._update_selected() self._update_style(recompute) self._update_selectable() def _update_selectable(self): selectable = self._get_selectable() for ref, (model, _) in self._models.copy().items(): self._apply_update([], {'selectable_rows': selectable}, model, ref) @param.depends('page_size', watch=True) def _update_max_page(self): length = self._length nrows = self.page_size or self.initial_page_size max_page = max(length//nrows + bool(length%nrows), 1) self.param.page.bounds = (1, max_page) for ref, (model, _) in self._models.copy().items(): self._apply_update([], {'max_page': max_page}, model, ref) def _clear_selection_remote_pagination(self, event): if not self._updating and self.selection and event.new is not event.old and self.pagination == 'remote': self.selection = [] def _update_selected(self, *events: param.parameterized.Event, indices=None): kwargs = {} if self.pagination == 'remote' and self.value is not None: # Compute integer indexes of the selected rows # on the displayed page index = self.value.iloc[self.selection].index indices = [] for ind in index.values: try: iloc = self._processed.index.get_loc(ind) self._validate_iloc(ind ,iloc) indices.append((ind, iloc)) except KeyError: continue nrows = self.page_size or self.initial_page_size start = (self.page - 1) * nrows end = start+nrows p_range = self._processed.index[start:end] kwargs['indices'] = [iloc - start for ind, iloc in indices if ind in p_range] super()._update_selected(*events, **kwargs) def _update_column(self, column: str, array: TDataColumn) -> None: import pandas as pd if self.pagination != 'remote': index = self._processed.index.values self.value.loc[index, column] = array with pd.option_context('mode.chained_assignment', None): self._processed[column] = array return nrows = self.page_size or self.initial_page_size start = (self.page - 1) * nrows end = start+nrows index = self._processed.iloc[start:end].index.values self.value.loc[index, column] = array with pd.option_context('mode.chained_assignment', None): self._processed.loc[index, column] = array def _map_indexes(self, indexes: list[int], existing: list[int] = [], add: bool = True) -> list[int]: if self.pagination == 'remote': nrows = self.page_size or self.initial_page_size start = (self.page-1)*nrows else: start = 0 ilocs = list(existing) index = self._processed.iloc[[start+ind for ind in indexes]].index for v in index.values: try: iloc = self.value.index.get_loc(v) self._validate_iloc(v, iloc) except KeyError: continue if add: ilocs.append(iloc) elif iloc in ilocs: ilocs.remove(iloc) return list(dict.fromkeys(ilocs)) def _update_expanded(self, expanded): self.expanded = self._map_indexes(expanded) def _update_selection(self, indices: list[int] | SelectionEvent): if isinstance(indices, list): selected = True ilocs: list[int] = [] inds = indices else: selected = indices.selected ilocs = [] if indices.flush else self.selection.copy() inds = indices.indices ilocs = self._map_indexes(inds, ilocs, add=selected) if isinstance(self.selectable, int) and not isinstance(self.selectable, bool): ilocs = ilocs[len(ilocs) - self.selectable:] self.selection = ilocs # type: ignore def _get_properties(self, doc: Document | None = None) -> dict[str, Any]: properties = super()._get_properties(doc) properties['configuration'] = self._get_configuration(properties['columns']) properties['cell_styles'] = self._get_style_data() properties['indexes'] = self.indexes if self.pagination: length = self._length page_size = self.page_size or self.initial_page_size properties['max_page'] = max(length//page_size + bool(length % page_size), 1) if isinstance(self.selectable, str) and self.selectable.startswith('checkbox'): properties['select_mode'] = 'checkbox' else: properties['select_mode'] = self.selectable return properties def _process_param_change(self, params): if 'theme' in params or 'stylesheets' in params: theme_url = self._get_theme(params.pop('theme', self.theme)) params['stylesheets'] = params.get('stylesheets', self.stylesheets) + [ ImportedStyleSheet(url=theme_url) ] params = Reactive._process_param_change(self, params) if 'disabled' in params: params['editable'] = not params.pop('disabled') and len(self.indexes) <= 1 if 'frozen_rows' in params: length = self._length params['frozen_rows'] = [ length+r if r < 0 else r for r in params['frozen_rows'] ] if 'hidden_columns' in params: import pandas as pd if not self.show_index and self.value is not None and not isinstance(self.value.index, pd.MultiIndex): params['hidden_columns'] = params['hidden_columns'] + [self.value.index.name or 'index'] if 'selectable_rows' in params: params['selectable_rows'] = self._get_selectable() return params def _get_model( self, doc: Document, root: Model | None = None, parent: Model | None = None, comm: Comm | None = None ) -> Model: Tabulator._widget_type = lazy_load( 'panel.models.tabulator', 'DataTabulator', isinstance(comm, JupyterComm), root ) model = super()._get_model(doc, root, parent, comm) root = root or model self._child_panels, removed, expanded = self._get_children() model.expanded = expanded model.children = self._get_model_children(doc, root, parent, comm) self._link_props(model, ['page', 'sorters', 'expanded', 'filters', 'page_size'], doc, root, comm) self._register_events('cell-click', 'table-edit', 'selection-change', model=model, doc=doc, comm=comm) return model def _get_filter_spec(self, column: TableColumn) -> FilterSpec: fspec: FilterSpec = {} if not self.header_filters or (isinstance(self.header_filters, dict) and column.field not in self.header_filters): return fspec elif self.header_filters == True: if column.field in self.indexes: if len(self.indexes) == 1: col = self.value.index else: col = self.value.index.get_level_values(self.indexes.index(column.field)) if col.dtype.kind in 'uif': fspec['headerFilter'] = 'number' elif col.dtype.kind == 'b': fspec['headerFilter'] = 'tickCross' fspec['headerFilterParams'] = {'tristate': True, 'indeterminateValue': None} elif isdatetime(col) or col.dtype.kind == 'M': fspec['headerFilter'] = False else: fspec['headerFilter'] = True elif isinstance(column.editor, DateEditor): # Datetime filtering currently broken with Tabulator 5.4.3 # Initial (empty) value of filter is passed to luxon.js # and causes error fspec['headerFilter'] = False else: fspec['headerFilter'] = True return fspec filter_type = self.header_filters[column.field] if isinstance(filter_type, dict): filter_params = dict(filter_type) filter_type = filter_params.pop('type', True) filter_func = filter_params.pop('func', None) filter_placeholder = filter_params.pop('placeholder', None) else: filter_params = {} filter_func = None filter_placeholder = None # Tabulator JS renamed select and autocomplete to list, and relies on # valuesLookup set to True to autopopulate the filter, instead of # values. This ensure backwards compatibility. if filter_type in ['select', 'autocomplete']: self.param.warning( f'The {filter_type!r} filter has been deprecated, use ' f'instead the "list" filter type to configure column {column.field!r}' ) filter_type = 'list' if filter_params.get('values', False) is True: self.param.warning( 'Setting "values" to True has been deprecated, instead ' f'set "valuesLookup" to True to configure column {column.field!r}' ) del filter_params['values'] filter_params['valuesLookup'] = True if filter_type == 'list': if not filter_params: filter_params = {'valuesLookup': True} if filter_func is None: filter_func = 'in' fspec['headerFilter'] = filter_type if filter_params: fspec['headerFilterParams'] = filter_params if filter_func: fspec['headerFilterFunc'] = filter_func if filter_placeholder: fspec['headerFilterPlaceholder'] = filter_placeholder return fspec def _config_columns(self, column_objs: list[TableColumn]) -> Sequence[ColumnSpec | GroupSpec]: column_objs = list(column_objs) groups: dict[str, GroupSpec] = {} columns: Sequence[ColumnSpec | GroupSpec] = [] selectable = self.selectable if self.row_content: columns.append({ "formatter": "expand" }) if isinstance(selectable, str) and selectable.startswith('checkbox'): title = "" if selectable.endswith('-single') else "rowSelection" columns.append({ "formatter": "rowSelection", "titleFormatter": title, "hozAlign": "center", "headerSort": False, "frozen": True, "width": 40, }) if isinstance(self.frozen_columns, dict): left_frozen_columns = [col for col in column_objs if self.frozen_columns.get(col.field, self.frozen_columns.get(column_objs.index(col))) == "left"] right_frozen_columns = [col for col in column_objs if self.frozen_columns.get(col.field, self.frozen_columns.get(column_objs.index(col))) == "right"] non_frozen_columns = [col for col in column_objs if col.field not in self.frozen_columns and column_objs.index(col) not in self.frozen_columns] ordered_columns = left_frozen_columns + non_frozen_columns + right_frozen_columns else: ordered_columns = [] for col in self.frozen_columns: if isinstance(col, int): ordered_columns.append(column_objs.pop(col)) else: cols = [c for c in column_objs if c.field == col] if cols: ordered_columns.append(cols[0]) column_objs.remove(cols[0]) ordered_columns += column_objs grouping = { group: [str(gc) for gc in group_cols] for group, group_cols in self.groups.items() } for i, column in enumerate(ordered_columns): field = str(column.field) index = self._renamed_cols[field] matching_groups = [ group for group, group_cols in grouping.items() if field in group_cols ] col_dict: ColumnSpec = dict(field=field) if isinstance(self.sortable, dict): col_dict['headerSort'] = self.sortable.get(field, True) elif not self.sortable: col_dict['headerSort'] = self.sortable if isinstance(self.text_align, str): col_dict['hozAlign'] = self.text_align # type: ignore elif field in self.text_align: col_dict['hozAlign'] = self.text_align[field] if isinstance(self.header_align, str): col_dict['headerHozAlign'] = self.header_align # type: ignore elif field in self.header_align: col_dict['headerHozAlign'] = self.header_align[field] # type: ignore formatter = self.formatters.get(field) if isinstance(formatter, str): col_dict['formatter'] = formatter elif isinstance(formatter, dict): formatter = dict(formatter) col_dict['formatter'] = formatter.pop('type') col_dict['formatterParams'] = formatter title_formatter = self.title_formatters.get(field) if isinstance(title_formatter, str): col_dict['titleFormatter'] = title_formatter elif isinstance(title_formatter, dict): title_formatter = dict(title_formatter) col_dict['titleFormatter'] = title_formatter.pop('type') col_dict['titleFormatterParams'] = title_formatter if field in self.indexes: if len(self.indexes) == 1: dtype = self.value.index.dtype else: dtype = self.value.index.get_level_values(self.indexes.index(field)).dtype else: dtype = self.value.dtypes[index] if dtype.kind == 'M': col_dict['sorter'] = 'timestamp' elif dtype.kind in 'iuf': col_dict['sorter'] = 'number' elif dtype.kind == 'b': col_dict['sorter'] = 'boolean' editor = self.editors.get(field) if field in self.editors and editor is None: col_dict['editable'] = False if isinstance(editor, str): col_dict['editor'] = editor elif isinstance(editor, dict): editor = dict(editor) col_dict['editor'] = editor.pop('type') col_dict['editorParams'] = editor if col_dict.get('editor') in ['select', 'autocomplete']: self.param.warning( f'The {col_dict["editor"]!r} editor has been deprecated, use ' f'instead the "list" editor type to configure column {field!r}' ) col_dict['editor'] = 'list' if col_dict.get('editorParams', {}).get('values', False) is True: del col_dict['editorParams']['values'] col_dict['editorParams']['valuesLookup'] = True if field in self.frozen_columns or i in self.frozen_columns: col_dict['frozen'] = True if isinstance(self.widths, dict) and isinstance(self.widths.get(field), str): col_dict['width'] = self.widths[field] col_dict.update(self._get_filter_spec(column)) if field in self.header_tooltips: col_dict["headerTooltip"] = self.header_tooltips[field] if isinstance(index, tuple): if columns: children = columns last = cast(GroupSpec, children[-1]) for group in index[:-1]: if 'title' in last and last['title'] == group: new = False children = last['columns'] else: new = True children.append({ 'columns': [], 'title': group, }) last = cast(GroupSpec, children[-1]) if new: children = last['columns'] children.append(col_dict) column.title = index[-1] elif matching_groups: group = matching_groups[0] if group in groups: groups[group]['columns'].append(col_dict) continue group_dict: GroupSpec = { 'title': group, 'columns': [col_dict] } groups[group] = group_dict columns.append(group_dict) else: columns.append(col_dict) return columns def _get_configuration(self, columns: list[TableColumn]) -> dict[str, Any]: """ Returns the Tabulator configuration. """ configuration = dict(self._configuration) if 'selectable' not in configuration: configuration['selectable'] = self.selectable if self.groups and 'columns' in configuration: raise ValueError("Groups must be defined either explicitly " "or via the configuration, not both.") user_columns = {v["field"]: v for v in configuration.get('columns', {})} configuration["columns"] = self._config_columns(columns) for idx, col in enumerate(columns): if (name := col.field) in user_columns: configuration["columns"][idx] |= user_columns[name] configuration['dataTree'] = self.hierarchical if self.sizing_mode in ('stretch_height', 'stretch_both'): configuration['maxHeight'] = '100%' elif self.height: configuration['height'] = self.height return configuration def download(self, filename: str = 'table.csv'): """ Triggers downloading of the table as a CSV or JSON. Parameters ---------- filename: str The filename to save the table as. """ for ref, (model, _) in self._models.copy().items(): self._apply_update({}, {'filename': filename}, model, ref) self._apply_update({}, {'download': not model.download}, model, ref) def download_menu(self, text_kwargs={}, button_kwargs={}): """ Returns a menu containing a TextInput and Button widget to set the filename and trigger a client-side download of the data. Parameters ---------- text_kwargs: dict Keyword arguments passed to the TextInput constructor button_kwargs: dict Keyword arguments passed to the Button constructor Returns ------- filename: TextInput The TextInput widget setting a filename. button: Button The Button that triggers a download. """ text_kwargs = dict(text_kwargs) if 'name' not in text_kwargs: text_kwargs['name'] = 'Filename' if 'value' not in text_kwargs: text_kwargs['value'] = 'table.csv' filename = TextInput(**text_kwargs) button_kwargs = dict(button_kwargs) if 'name' not in button_kwargs: button_kwargs['name'] = 'Download' button = Button(**button_kwargs) button.js_on_click({'table': self, 'filename': filename}, code=""" table.filename = filename.value table.download = !table.download """) return filename, button def on_edit(self, callback: Callable[[TableEditEvent], None]): """ Register a callback to be executed when a cell is edited. Whenever a cell is edited on_edit callbacks are called with a TableEditEvent as the first argument containing the column, row and value of the edited cell. Parameters ---------- callback: (callable) The callback to run on edit events. """ self._on_edit_callbacks.append(callback) def on_click(self, callback: Callable[[CellClickEvent], None], column: str | None = None): """ Register a callback to be executed when any cell is clicked. The callback is given a CellClickEvent declaring the column and row of the cell that was clicked. Parameters ---------- callback: (callable) The callback to run on edit events. column: (str) Optional argument restricting the callback to a specific column. """ if column not in self._on_click_callbacks: self._on_click_callbacks[column] = [] self._on_click_callbacks[column].append(callback) @property def current_view(self) -> pd.DataFrame: """ Returns the current view of the table after filtering and sorting are applied. """ df = self._processed if self.pagination == 'remote': return df df = self._filter_dataframe(df, header_filters=True, internal_filters=False) return self._sort_df(df)