Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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351 lines
11 KiB
351 lines
11 KiB
from abc import ABCMeta, abstractmethod
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import sys
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from typing import IO, TYPE_CHECKING, List, Optional, Tuple, Union
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from pandas._config import get_option
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from pandas._typing import Dtype, FrameOrSeries
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from pandas.core.indexes.api import Index
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from pandas.io.formats import format as fmt
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from pandas.io.formats.printing import pprint_thing
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if TYPE_CHECKING:
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from pandas.core.series import Series # noqa: F401
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def _put_str(s: Union[str, Dtype], space: int) -> str:
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"""
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Make string of specified length, padding to the right if necessary.
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Parameters
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----------
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s : Union[str, Dtype]
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String to be formatted.
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space : int
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Length to force string to be of.
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Returns
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-------
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str
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String coerced to given length.
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Examples
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--------
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>>> pd.io.formats.info._put_str("panda", 6)
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'panda '
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>>> pd.io.formats.info._put_str("panda", 4)
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'pand'
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"""
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return str(s)[:space].ljust(space)
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def _sizeof_fmt(num: Union[int, float], size_qualifier: str) -> str:
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"""
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Return size in human readable format.
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Parameters
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----------
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num : int
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Size in bytes.
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size_qualifier : str
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Either empty, or '+' (if lower bound).
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Returns
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-------
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str
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Size in human readable format.
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Examples
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--------
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>>> _sizeof_fmt(23028, '')
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'22.5 KB'
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>>> _sizeof_fmt(23028, '+')
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'22.5+ KB'
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"""
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for x in ["bytes", "KB", "MB", "GB", "TB"]:
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if num < 1024.0:
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return f"{num:3.1f}{size_qualifier} {x}"
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num /= 1024.0
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return f"{num:3.1f}{size_qualifier} PB"
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class BaseInfo(metaclass=ABCMeta):
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def __init__(
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self,
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data: FrameOrSeries,
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verbose: Optional[bool] = None,
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buf: Optional[IO[str]] = None,
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max_cols: Optional[int] = None,
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memory_usage: Optional[Union[bool, str]] = None,
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null_counts: Optional[bool] = None,
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):
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if buf is None: # pragma: no cover
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buf = sys.stdout
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if memory_usage is None:
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memory_usage = get_option("display.memory_usage")
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self.data = data
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self.verbose = verbose
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self.buf = buf
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self.max_cols = max_cols
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self.memory_usage = memory_usage
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self.null_counts = null_counts
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@abstractmethod
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def _get_mem_usage(self, deep: bool) -> int:
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"""
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Get memory usage in bytes.
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Parameters
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----------
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deep : bool
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If True, introspect the data deeply by interrogating object dtypes
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for system-level memory consumption, and include it in the returned
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values.
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Returns
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-------
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mem_usage : int
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Object's total memory usage in bytes.
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"""
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pass
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@abstractmethod
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def _get_ids_and_dtypes(self) -> Tuple["Index", "Series"]:
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"""
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Get column names and dtypes.
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Returns
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-------
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ids : Index
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DataFrame's column names.
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dtypes : Series
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Dtype of each of the DataFrame's columns.
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"""
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pass
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@abstractmethod
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def _verbose_repr(
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self, lines: List[str], ids: "Index", dtypes: "Series", show_counts: bool
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) -> None:
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"""
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Append name, non-null count (optional), and dtype for each column to `lines`.
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Parameters
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----------
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lines : List[str]
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Lines that will contain `info` representation.
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ids : Index
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The DataFrame's column names.
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dtypes : Series
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The DataFrame's columns' dtypes.
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show_counts : bool
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If True, count of non-NA cells for each column will be appended to `lines`.
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"""
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pass
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@abstractmethod
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def _non_verbose_repr(self, lines: List[str], ids: "Index") -> None:
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"""
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Append short summary of columns' names to `lines`.
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Parameters
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----------
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lines : List[str]
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Lines that will contain `info` representation.
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ids : Index
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The DataFrame's column names.
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"""
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pass
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def info(self) -> None:
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"""
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Print a concise summary of a %(klass)s.
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This method prints information about a %(klass)s including
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the index dtype%(type_sub)s, non-null values and memory usage.
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Parameters
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----------
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data : %(klass)s
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%(klass)s to print information about.
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verbose : bool, optional
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Whether to print the full summary. By default, the setting in
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``pandas.options.display.max_info_columns`` is followed.
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buf : writable buffer, defaults to sys.stdout
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Where to send the output. By default, the output is printed to
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sys.stdout. Pass a writable buffer if you need to further process
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the output.
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%(max_cols_sub)s
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memory_usage : bool, str, optional
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Specifies whether total memory usage of the %(klass)s
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elements (including the index) should be displayed. By default,
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this follows the ``pandas.options.display.memory_usage`` setting.
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True always show memory usage. False never shows memory usage.
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A value of 'deep' is equivalent to "True with deep introspection".
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Memory usage is shown in human-readable units (base-2
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representation). Without deep introspection a memory estimation is
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made based in column dtype and number of rows assuming values
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consume the same memory amount for corresponding dtypes. With deep
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memory introspection, a real memory usage calculation is performed
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at the cost of computational resources.
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null_counts : bool, optional
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Whether to show the non-null counts. By default, this is shown
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only if the %(klass)s is smaller than
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``pandas.options.display.max_info_rows`` and
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``pandas.options.display.max_info_columns``. A value of True always
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shows the counts, and False never shows the counts.
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Returns
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-------
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None
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This method prints a summary of a %(klass)s and returns None.
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See Also
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--------
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%(see_also_sub)s
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Examples
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--------
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%(examples_sub)s
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"""
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lines = []
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lines.append(str(type(self.data)))
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lines.append(self.data.index._summary())
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ids, dtypes = self._get_ids_and_dtypes()
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col_count = len(ids)
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if col_count == 0:
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lines.append(f"Empty {type(self.data).__name__}")
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fmt.buffer_put_lines(self.buf, lines)
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return
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# hack
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max_cols = self.max_cols
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if max_cols is None:
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max_cols = get_option("display.max_info_columns", col_count + 1)
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max_rows = get_option("display.max_info_rows", len(self.data) + 1)
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if self.null_counts is None:
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show_counts = (col_count <= max_cols) and (len(self.data) < max_rows)
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else:
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show_counts = self.null_counts
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exceeds_info_cols = col_count > max_cols
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if self.verbose:
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self._verbose_repr(lines, ids, dtypes, show_counts)
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elif self.verbose is False: # specifically set to False, not necessarily None
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self._non_verbose_repr(lines, ids)
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else:
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if exceeds_info_cols:
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self._non_verbose_repr(lines, ids)
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else:
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self._verbose_repr(lines, ids, dtypes, show_counts)
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# groupby dtype.name to collect e.g. Categorical columns
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counts = dtypes.value_counts().groupby(lambda x: x.name).sum()
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collected_dtypes = [f"{k[0]}({k[1]:d})" for k in sorted(counts.items())]
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lines.append(f"dtypes: {', '.join(collected_dtypes)}")
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if self.memory_usage:
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# append memory usage of df to display
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size_qualifier = ""
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if self.memory_usage == "deep":
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deep = True
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else:
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# size_qualifier is just a best effort; not guaranteed to catch
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# all cases (e.g., it misses categorical data even with object
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# categories)
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deep = False
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if "object" in counts or self.data.index._is_memory_usage_qualified():
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size_qualifier = "+"
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mem_usage = self._get_mem_usage(deep=deep)
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lines.append(f"memory usage: {_sizeof_fmt(mem_usage, size_qualifier)}\n")
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fmt.buffer_put_lines(self.buf, lines)
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class DataFrameInfo(BaseInfo):
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def _get_mem_usage(self, deep: bool) -> int:
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return self.data.memory_usage(index=True, deep=deep).sum()
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def _get_ids_and_dtypes(self) -> Tuple["Index", "Series"]:
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return self.data.columns, self.data.dtypes
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def _verbose_repr(
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self, lines: List[str], ids: "Index", dtypes: "Series", show_counts: bool
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) -> None:
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col_count = len(ids)
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lines.append(f"Data columns (total {col_count} columns):")
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id_head = " # "
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column_head = "Column"
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col_space = 2
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max_col = max(len(pprint_thing(k)) for k in ids)
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len_column = len(pprint_thing(column_head))
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space = max(max_col, len_column) + col_space
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max_id = len(pprint_thing(col_count))
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len_id = len(pprint_thing(id_head))
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space_num = max(max_id, len_id) + col_space
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header = _put_str(id_head, space_num) + _put_str(column_head, space)
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if show_counts:
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counts = self.data.count()
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if col_count != len(counts): # pragma: no cover
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raise AssertionError(
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f"Columns must equal counts ({col_count} != {len(counts)})"
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)
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count_header = "Non-Null Count"
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len_count = len(count_header)
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non_null = " non-null"
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max_count = max(len(pprint_thing(k)) for k in counts) + len(non_null)
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space_count = max(len_count, max_count) + col_space
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count_temp = "{count}" + non_null
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else:
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count_header = ""
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space_count = len(count_header)
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len_count = space_count
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count_temp = "{count}"
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dtype_header = "Dtype"
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len_dtype = len(dtype_header)
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max_dtypes = max(len(pprint_thing(k)) for k in dtypes)
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space_dtype = max(len_dtype, max_dtypes)
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header += _put_str(count_header, space_count) + _put_str(
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dtype_header, space_dtype
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)
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lines.append(header)
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lines.append(
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_put_str("-" * len_id, space_num)
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+ _put_str("-" * len_column, space)
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+ _put_str("-" * len_count, space_count)
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+ _put_str("-" * len_dtype, space_dtype)
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)
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for i, col in enumerate(ids):
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dtype = dtypes.iloc[i]
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col = pprint_thing(col)
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line_no = _put_str(f" {i}", space_num)
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count = ""
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if show_counts:
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count = counts.iloc[i]
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lines.append(
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line_no
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+ _put_str(col, space)
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+ _put_str(count_temp.format(count=count), space_count)
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+ _put_str(dtype, space_dtype)
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)
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def _non_verbose_repr(self, lines: List[str], ids: "Index") -> None:
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lines.append(ids._summary(name="Columns"))
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