145 lines
4.5 KiB
Python
145 lines
4.5 KiB
Python
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from functools import reduce
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from itertools import groupby
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from operator import add
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from dash import html
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def _generate_table_from_df(
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cls,
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df,
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float_format=None,
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columns=None,
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header=True,
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index=False,
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index_label=None,
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date_format=None,
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**table_kwargs
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):
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"""
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Generate a Table component from a dataframe.
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Parameters
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----------
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df : pandas.DataFrame
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DataFrame to render as a table.
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float_format : str, optional
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Format to use for floating point numbers.
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columns : sequence, optional
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Columns to render.
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header : boolean or list(str) or dict(str: str), optional
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Write out the column names. If a list of strings is given it is assumed
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to be aliases for the columns names (and hence must be the same length
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as df.columns). A dict can be passed to rename some columns, the format
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is {'<current_name>': '<new_name>'}. The dictionary need not have an
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entry for every column.
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index : boolean, optional
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Render the row names (index).
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index_label : str, optional
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Column label for index column if desired. If None is passed, but both
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header and index are True, then the index name is used.
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date_format : str, optional
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Format string for datetime objects.
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**table_kwargs : Keyword arguments
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Additional arguments to pass to the table component. See
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dash_bootstrap_components.Table for details.
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"""
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import numpy as np
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import pandas as pd
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if columns is not None:
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df = df.loc[:, columns]
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if float_format is not None:
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for c in df.select_dtypes(["float"]).columns:
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df[c] = df[c].map(lambda x: "{1:{0}}".format(float_format, x))
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if date_format is not None:
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for c in df.select_dtypes(["datetime"]).columns:
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df[c] = df[c].map(lambda x: x.strftime(date_format))
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if index:
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df = df.reset_index()
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if index_label is not None:
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df = df.rename(columns={"index": index_label})
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if header:
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if isinstance(header, (tuple, list, np.ndarray, pd.Index)):
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try:
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df.columns = header
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except ValueError:
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raise ValueError(
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"If specifying column names with a sequence, the number "
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"of names must exactly match the number of columns."
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)
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elif isinstance(header, dict):
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df = df.rename(columns=header)
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# Get the actual headers
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n_levels = df.columns.nlevels
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header_values = [
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list(df.columns.get_level_values(level))
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for level in range(n_levels)
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]
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# The sizes of consecutive header groups at each level
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header_spans = [
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[len(list(group)) for _, group in groupby(level_values)]
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for level_values in header_values
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]
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# The positions of header changes for each level as an integer
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header_breaks = [
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[sum(level_spans[:i]) for i in range(1, len(level_spans) + 1)]
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for level_spans in header_spans
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]
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# Include breaks from higher levels
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header_breaks = [
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sorted(set(reduce(add, header_breaks[:level])).union({0}))
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for level in range(1, n_levels + 1)
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]
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# Go from header break positions back to cell spans
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header_spans = [
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reversed(
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[
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level_breaks[i] - level_breaks[i - 1]
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for i in range(len(level_breaks) - 1, 0, -1)
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]
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)
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for level_breaks in header_breaks
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]
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table = [
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html.Thead(
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[
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html.Tr(
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children=[
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html.Th(
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header_values[level][pos],
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colSpan=span,
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)
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for pos, span in zip(
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header_breaks[level], header_spans[level]
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)
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]
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)
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for level in range(n_levels)
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]
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)
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]
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else:
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table = []
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table.append(
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html.Tbody(
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[
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html.Tr(
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[html.Td(df.iloc[i, j]) for j in range(len(df.columns))]
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)
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for i in range(len(df))
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]
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)
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)
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return cls(table, **table_kwargs)
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