Source code for astropy.table.table

# Licensed under a 3-clause BSD style license - see LICENSE.rst
import itertools
import sys
import types
import warnings
import weakref
from collections import OrderedDict, defaultdict
from collections.abc import Mapping
from copy import deepcopy

import numpy as np
from numpy import ma

from astropy import log
from astropy.io.registry import UnifiedReadWriteMethod
from astropy.units import Quantity, QuantityInfo
from astropy.utils import (
    ShapedLikeNDArray,
    deprecated,
    isiterable,
)
from astropy.utils.compat import COPY_IF_NEEDED, NUMPY_LT_1_25
from astropy.utils.console import color_print
from astropy.utils.data_info import BaseColumnInfo, DataInfo, MixinInfo
from astropy.utils.decorators import format_doc
from astropy.utils.exceptions import AstropyUserWarning
from astropy.utils.masked import Masked
from astropy.utils.metadata import MetaAttribute, MetaData

from . import conf, groups
from .column import (
    BaseColumn,
    Column,
    FalseArray,
    MaskedColumn,
    _auto_names,
    _convert_sequence_data_to_array,
    col_copy,
)
from .connect import TableRead, TableWrite
from .index import (
    Index,
    SlicedIndex,
    TableILoc,
    TableIndices,
    TableLoc,
    TableLocIndices,
    _IndexModeContext,
    get_index,
)
from .info import TableInfo
from .mixins.registry import get_mixin_handler
from .ndarray_mixin import NdarrayMixin  # noqa: F401
from .pprint import TableFormatter
from .row import Row

_implementation_notes = """
This string has informal notes concerning Table implementation for developers.

Things to remember:

- Table has customizable attributes ColumnClass, Column, MaskedColumn.
  Table.Column is normally just column.Column (same w/ MaskedColumn)
  but in theory they can be different.  Table.ColumnClass is the default
  class used to create new non-mixin columns, and this is a function of
  the Table.masked attribute.  Column creation / manipulation in a Table
  needs to respect these.

- Column objects that get inserted into the Table.columns attribute must
  have the info.parent_table attribute set correctly.  Beware just dropping
  an object into the columns dict since an existing column may
  be part of another Table and have parent_table set to point at that
  table.  Dropping that column into `columns` of this Table will cause
  a problem for the old one so the column object needs to be copied (but
  not necessarily the data).

  Currently replace_column is always making a copy of both object and
  data if parent_table is set.  This could be improved but requires a
  generic way to copy a mixin object but not the data.

- Be aware of column objects that have indices set.

- `cls.ColumnClass` is a property that effectively uses the `masked` attribute
  to choose either `cls.Column` or `cls.MaskedColumn`.
"""

__doctest_skip__ = [
    "Table.read",
    "Table.write",
    "Table._read",
    "Table.convert_bytestring_to_unicode",
    "Table.convert_unicode_to_bytestring",
]

__doctest_requires__ = {"*pandas": ["pandas>=1.1"]}

_pprint_docs = """
    {__doc__}

    Parameters
    ----------
    max_lines : int or None
        Maximum number of lines in table output.

    max_width : int or None
        Maximum character width of output.

    show_name : bool
        Include a header row for column names. Default is True.

    show_unit : bool
        Include a header row for unit.  Default is to show a row
        for units only if one or more columns has a defined value
        for the unit.

    show_dtype : bool
        Include a header row for column dtypes. Default is False.

    align : str or list or tuple or None
        Left/right alignment of columns. Default is right (None) for all
        columns. Other allowed values are '>', '<', '^', and '0=' for
        right, left, centered, and 0-padded, respectively. A list of
        strings can be provided for alignment of tables with multiple
        columns.
    """

_pformat_docs = """
    {__doc__}

    Parameters
    ----------
    max_lines : int or None
        Maximum number of rows to output

    max_width : int or None
        Maximum character width of output

    show_name : bool
        Include a header row for column names. Default is True.

    show_unit : bool
        Include a header row for unit.  Default is to show a row
        for units only if one or more columns has a defined value
        for the unit.

    show_dtype : bool
        Include a header row for column dtypes. Default is True.

    html : bool
        Format the output as an HTML table. Default is False.

    tableid : str or None
        An ID tag for the table; only used if html is set.  Default is
        "table{id}", where id is the unique integer id of the table object,
        id(self)

    align : str or list or tuple or None
        Left/right alignment of columns. Default is right (None) for all
        columns. Other allowed values are '>', '<', '^', and '0=' for
        right, left, centered, and 0-padded, respectively. A list of
        strings can be provided for alignment of tables with multiple
        columns.

    tableclass : str or list of str or None
        CSS classes for the table; only used if html is set.  Default is
        None.

    Returns
    -------
    lines : list
        Formatted table as a list of strings.
    """


[docs] class TableReplaceWarning(UserWarning): """ Warning class for cases when a table column is replaced via the Table.__setitem__ syntax e.g. t['a'] = val. This does not inherit from AstropyWarning because we want to use stacklevel=3 to show the user where the issue occurred in their code. """
def descr(col): """Array-interface compliant full description of a column. This returns a 3-tuple (name, type, shape) that can always be used in a structured array dtype definition. """ col_dtype = "O" if (col.info.dtype is None) else col.info.dtype col_shape = col.shape[1:] if hasattr(col, "shape") else () return (col.info.name, col_dtype, col_shape) def has_info_class(obj, cls): """Check if the object's info is an instance of cls.""" # We check info on the class of the instance, since on the instance # itself accessing 'info' has side effects in that it sets # obj.__dict__['info'] if it does not exist already. return isinstance(getattr(obj.__class__, "info", None), cls) def _get_names_from_list_of_dict(rows): """Return list of column names if ``rows`` is a list of dict that defines table data. If rows is not a list of dict then return None. """ if rows is None: return None names = set() for row in rows: if not isinstance(row, Mapping): return None names.update(row) return list(names) # Note to future maintainers: when transitioning this to dict # be sure to change the OrderedDict ref(s) in Row and in __len__().
[docs] class TableColumns(OrderedDict): """OrderedDict subclass for a set of columns. This class enhances item access to provide convenient access to columns by name or index, including slice access. It also handles renaming of columns. The initialization argument ``cols`` can be a list of ``Column`` objects or any structure that is valid for initializing a Python dict. This includes a dict, list of (key, val) tuples or [key, val] lists, etc. Parameters ---------- cols : dict, list, tuple; optional Column objects as data structure that can init dict (see above) """ def __init__(self, cols={}): if isinstance(cols, (list, tuple)): # `cols` should be a list of two-tuples, but it is allowed to have # columns (BaseColumn or mixins) in the list. newcols = [] for col in cols: if has_info_class(col, BaseColumnInfo): newcols.append((col.info.name, col)) else: newcols.append(col) cols = newcols super().__init__(cols) def __getitem__(self, item): """Get items from a TableColumns object. :: tc = TableColumns(cols=[Column(name='a'), Column(name='b'), Column(name='c')]) tc['a'] # Column('a') tc[1] # Column('b') tc['a', 'b'] # <TableColumns names=('a', 'b')> tc[1:3] # <TableColumns names=('b', 'c')> """ if isinstance(item, str): return OrderedDict.__getitem__(self, item) elif isinstance(item, (int, np.integer)): return list(self.values())[item] elif ( isinstance(item, np.ndarray) and item.shape == () and item.dtype.kind == "i" ): return list(self.values())[item.item()] elif isinstance(item, tuple): return self.__class__([self[x] for x in item]) elif isinstance(item, slice): return self.__class__([self[x] for x in list(self)[item]]) else: raise IndexError( f"Illegal key or index value for {type(self).__name__} object" ) def __setitem__(self, item, value, validated=False): """ Set item in this dict instance, but do not allow directly replacing an existing column unless it is already validated (and thus is certain to not corrupt the table). NOTE: it is easily possible to corrupt a table by directly *adding* a new key to the TableColumns attribute of a Table, e.g. ``t.columns['jane'] = 'doe'``. """ if item in self and not validated: raise ValueError( f"Cannot replace column '{item}'. Use Table.replace_column() instead." ) super().__setitem__(item, value) def __repr__(self): names = (f"'{x}'" for x in self.keys()) return f"<{self.__class__.__name__} names=({','.join(names)})>" def _rename_column(self, name, new_name): if name == new_name: return if new_name in self: raise KeyError(f"Column {new_name} already exists") # Rename column names in pprint include/exclude attributes as needed parent_table = self[name].info.parent_table if parent_table is not None: parent_table.pprint_exclude_names._rename(name, new_name) parent_table.pprint_include_names._rename(name, new_name) mapper = {name: new_name} new_names = [mapper.get(name, name) for name in self] cols = list(self.values()) self.clear() super().update(zip(new_names, cols)) def __delitem__(self, name): # Remove column names from pprint include/exclude attributes as needed. # __delitem__ also gets called for pop() and popitem(). parent_table = self[name].info.parent_table if parent_table is not None: # _remove() method does not require that `name` is in the attribute parent_table.pprint_exclude_names._remove(name) parent_table.pprint_include_names._remove(name) return super().__delitem__(name)
[docs] def isinstance(self, cls): """ Return a list of columns which are instances of the specified classes. Parameters ---------- cls : class or tuple thereof Column class (including mixin) or tuple of Column classes. Returns ------- col_list : list of `Column` List of Column objects which are instances of given classes. """ cols = [col for col in self.values() if isinstance(col, cls)] return cols
[docs] def not_isinstance(self, cls): """ Return a list of columns which are not instances of the specified classes. Parameters ---------- cls : class or tuple thereof Column class (including mixin) or tuple of Column classes. Returns ------- col_list : list of `Column` List of Column objects which are not instances of given classes. """ cols = [col for col in self.values() if not isinstance(col, cls)] return cols
# When the deprecation period of setdefault() and update() is over then they # need to be rewritten to raise an error, not removed.
[docs] @deprecated( since="6.1", alternative="t.setdefault()", name="t.columns.setdefault()" ) def setdefault(self, key, default): return super().setdefault(key, default)
[docs] @deprecated(since="6.1", alternative="t.update()", name="t.columns.update()") def update(self, *args, **kwargs): return super().update(*args, **kwargs)
[docs] class TableAttribute(MetaAttribute): """ Descriptor to define a custom attribute for a Table subclass. The value of the ``TableAttribute`` will be stored in a dict named ``__attributes__`` that is stored in the table ``meta``. The attribute can be accessed and set in the usual way, and it can be provided when creating the object. Defining an attribute by this mechanism ensures that it will persist if the table is sliced or serialized, for example as a pickle or ECSV file. See the `~astropy.utils.metadata.MetaAttribute` documentation for additional details. Parameters ---------- default : object Default value for attribute Examples -------- >>> from astropy.table import Table, TableAttribute >>> class MyTable(Table): ... identifier = TableAttribute(default=1) >>> t = MyTable(identifier=10) >>> t.identifier 10 >>> t.meta OrderedDict([('__attributes__', {'identifier': 10})]) """
[docs] class PprintIncludeExclude(TableAttribute): """Maintain tuple that controls table column visibility for print output. This is a descriptor that inherits from MetaAttribute so that the attribute value is stored in the table meta['__attributes__']. This gets used for the ``pprint_include_names`` and ``pprint_exclude_names`` Table attributes. """ def __get__(self, instance, owner_cls): """Get the attribute. This normally returns an instance of this class which is stored on the owner object. """ # For getting from class not an instance if instance is None: return self # If not already stored on `instance`, make a copy of the class # descriptor object and put it onto the instance. value = instance.__dict__.get(self.name) if value is None: value = deepcopy(self) instance.__dict__[self.name] = value # We set _instance_ref on every call, since if one makes copies of # instances, this attribute will be copied as well, which will lose the # reference. value._instance_ref = weakref.ref(instance) return value def __set__(self, instance, names): """Set value of ``instance`` attribute to ``names``. Parameters ---------- instance : object Instance that owns the attribute names : None, str, list, tuple Column name(s) to store, or None to clear """ if isinstance(names, str): names = [names] if names is None: # Remove attribute value from the meta['__attributes__'] dict. # Subsequent access will just return None. delattr(instance, self.name) else: # This stores names into instance.meta['__attributes__'] as tuple return super().__set__(instance, tuple(names))
[docs] def __call__(self): """Get the value of the attribute. Returns ------- names : None, tuple Include/exclude names """ # Get the value from instance.meta['__attributes__'] instance = self._instance_ref() return super().__get__(instance, instance.__class__)
def __repr__(self): if hasattr(self, "_instance_ref"): out = f"<{self.__class__.__name__} name={self.name} value={self()}>" else: out = super().__repr__() return out def _add_remove_setup(self, names): """Common setup for add and remove. - Coerce attribute value to a list - Coerce names into a list - Get the parent table instance """ names = [names] if isinstance(names, str) else list(names) # Get the value. This is the same as self() but we need `instance` here. instance = self._instance_ref() value = super().__get__(instance, instance.__class__) value = [] if value is None else list(value) return instance, names, value
[docs] def add(self, names): """Add ``names`` to the include/exclude attribute. Parameters ---------- names : str, list, tuple Column name(s) to add """ instance, names, value = self._add_remove_setup(names) value.extend(name for name in names if name not in value) super().__set__(instance, tuple(value))
[docs] def remove(self, names): """Remove ``names`` from the include/exclude attribute. Parameters ---------- names : str, list, tuple Column name(s) to remove """ self._remove(names, raise_exc=True)
def _remove(self, names, raise_exc=False): """Remove ``names`` with optional checking if they exist.""" instance, names, value = self._add_remove_setup(names) # Return now if there are no attributes and thus no action to be taken. if not raise_exc and "__attributes__" not in instance.meta: return # Remove one by one, optionally raising an exception if name is missing. for name in names: if name in value: value.remove(name) # Using the list.remove method elif raise_exc: raise ValueError(f"{name} not in {self.name}") # Change to either None or a tuple for storing back to attribute value = None if value == [] else tuple(value) self.__set__(instance, value) def _rename(self, name, new_name): """Rename ``name`` to ``new_name`` if ``name`` is in the list.""" names = self() or () if name in names: new_names = list(names) new_names[new_names.index(name)] = new_name self.set(new_names)
[docs] def set(self, names): """Set value of include/exclude attribute to ``names``. Parameters ---------- names : None, str, list, tuple Column name(s) to store, or None to clear """ class _Context: def __init__(self, descriptor_self): self.descriptor_self = descriptor_self self.names_orig = descriptor_self() def __enter__(self): pass def __exit__(self, type, value, tb): descriptor_self = self.descriptor_self instance = descriptor_self._instance_ref() descriptor_self.__set__(instance, self.names_orig) def __repr__(self): return repr(self.descriptor_self) ctx = _Context(descriptor_self=self) instance = self._instance_ref() self.__set__(instance, names) return ctx
[docs] class Table: """A class to represent tables of heterogeneous data. `~astropy.table.Table` provides a class for heterogeneous tabular data. A key enhancement provided by the `~astropy.table.Table` class over e.g. a `numpy` structured array is the ability to easily modify the structure of the table by adding or removing columns, or adding new rows of data. In addition table and column metadata are fully supported. `~astropy.table.Table` differs from `~astropy.nddata.NDData` by the assumption that the input data consists of columns of homogeneous data, where each column has a unique identifier and may contain additional metadata such as the data unit, format, and description. See also: https://docs.astropy.org/en/stable/table/ Parameters ---------- data : numpy ndarray, dict, list, table-like object, optional Data to initialize table. masked : bool, optional Specify whether the table is masked. names : list, optional Specify column names. dtype : list, optional Specify column data types. meta : dict, optional Metadata associated with the table. copy : bool, optional Copy the input column data and make a deep copy of the input meta. Default is True. rows : numpy ndarray, list of list, optional Row-oriented data for table instead of ``data`` argument. copy_indices : bool, optional Copy any indices in the input data. Default is True. units : list, dict, optional List or dict of units to apply to columns. descriptions : list, dict, optional List or dict of descriptions to apply to columns. **kwargs : dict, optional Additional keyword args when converting table-like object. """ meta = MetaData(copy=False) # Define class attributes for core container objects to allow for subclass # customization. Row = Row Column = Column MaskedColumn = MaskedColumn TableColumns = TableColumns TableFormatter = TableFormatter # Unified I/O read and write methods from .connect read = UnifiedReadWriteMethod(TableRead) write = UnifiedReadWriteMethod(TableWrite) pprint_exclude_names = PprintIncludeExclude() pprint_include_names = PprintIncludeExclude()
[docs] def as_array(self, keep_byteorder=False, names=None): """ Return a new copy of the table in the form of a structured np.ndarray or np.ma.MaskedArray object (as appropriate). Parameters ---------- keep_byteorder : bool, optional By default the returned array has all columns in native byte order. However, if this option is `True` this preserves the byte order of all columns (if any are non-native). names : list, optional: List of column names to include for returned structured array. Default is to include all table columns. Returns ------- table_array : array or `~numpy.ma.MaskedArray` Copy of table as a numpy structured array. ndarray for unmasked or `~numpy.ma.MaskedArray` for masked. """ masked = self.masked or self.has_masked_columns or self.has_masked_values empty_init = ma.empty if masked else np.empty if len(self.columns) == 0: return empty_init(0, dtype=None) dtype = [] cols = self.columns.values() if names is not None: cols = [col for col in cols if col.info.name in names] for col in cols: col_descr = descr(col) if not (col.info.dtype.isnative or keep_byteorder): new_dt = np.dtype(col_descr[1]).newbyteorder("=") col_descr = (col_descr[0], new_dt, col_descr[2]) dtype.append(col_descr) data = empty_init(len(self), dtype=dtype) for col in cols: # When assigning from one array into a field of a structured array, # Numpy will automatically swap those columns to their destination # byte order where applicable data[col.info.name] = col # For masked out, masked mixin columns need to set output mask attribute. if masked and has_info_class(col, MixinInfo) and hasattr(col, "mask"): data[col.info.name].mask = col.mask # Propagate the fill_value from the table column to the output array. # If this is not done, then the output array will use numpy.ma's default # fill values (999999 for ints, 1E20 for floats, "N/A" for strings) if masked and hasattr(col, "fill_value"): data[col.info.name].fill_value = col.fill_value return data
def __init__( self, data=None, masked=False, names=None, dtype=None, meta=None, copy=True, rows=None, copy_indices=True, units=None, descriptions=None, **kwargs, ): # Set up a placeholder empty table self._set_masked(masked) self.columns = self.TableColumns() self.formatter = self.TableFormatter() self._copy_indices = True # copy indices from this Table by default self._init_indices = copy_indices # whether to copy indices in init self.primary_key = None # Must copy if dtype are changing if not copy and dtype is not None: raise ValueError("Cannot specify dtype when copy=False") # Specifies list of names found for the case of initializing table with # a list of dict. If data are not list of dict then this is None. names_from_list_of_dict = None # Row-oriented input, e.g. list of lists or list of tuples, list of # dict, Row instance. Set data to something that the subsequent code # will parse correctly. if rows is not None: if data is not None: raise ValueError("Cannot supply both `data` and `rows` values") if isinstance(rows, types.GeneratorType): # Without this then the all(..) test below uses up the generator rows = list(rows) # Get column names if `rows` is a list of dict, otherwise this is None names_from_list_of_dict = _get_names_from_list_of_dict(rows) if names_from_list_of_dict: data = rows elif isinstance(rows, self.Row): data = rows else: data = list(zip(*rows)) # Infer the type of the input data and set up the initialization # function, number of columns, and potentially the default col names default_names = None # Handle custom (subclass) table attributes that are stored in meta. # These are defined as class attributes using the TableAttribute # descriptor. Any such attributes get removed from kwargs here and # stored for use after the table is otherwise initialized. Any values # provided via kwargs will have precedence over existing values from # meta (e.g. from data as a Table or meta via kwargs). meta_table_attrs = {} if kwargs: for attr in list(kwargs): descr = getattr(self.__class__, attr, None) if isinstance(descr, TableAttribute): meta_table_attrs[attr] = kwargs.pop(attr) if hasattr(data, "__astropy_table__"): # Data object implements the __astropy_table__ interface method. # Calling that method returns an appropriate instance of # self.__class__ and respects the `copy` arg. The returned # Table object should NOT then be copied. data = data.__astropy_table__(self.__class__, copy, **kwargs) copy = COPY_IF_NEEDED elif kwargs: raise TypeError( f"__init__() got unexpected keyword argument {next(iter(kwargs.keys()))!r}" ) if isinstance(data, np.ndarray) and data.shape == (0,) and not data.dtype.names: data = None if isinstance(data, self.Row): data = data._table[data._index : data._index + 1] if isinstance(data, (list, tuple)): # Get column names from `data` if it is a list of dict, otherwise this is None. # This might be previously defined if `rows` was supplied as an init arg. names_from_list_of_dict = ( names_from_list_of_dict or _get_names_from_list_of_dict(data) ) if names_from_list_of_dict: init_func = self._init_from_list_of_dicts n_cols = len(names_from_list_of_dict) else: init_func = self._init_from_list n_cols = len(data) elif isinstance(data, np.ndarray): if data.dtype.names: init_func = self._init_from_ndarray # _struct n_cols = len(data.dtype.names) default_names = data.dtype.names else: init_func = self._init_from_ndarray # _homog if data.shape == (): raise ValueError("Can not initialize a Table with a scalar") elif len(data.shape) == 1: data = data[np.newaxis, :] n_cols = data.shape[1] elif isinstance(data, Mapping): init_func = self._init_from_dict default_names = list(data) n_cols = len(default_names) elif isinstance(data, Table): # If user-input meta is None then use data.meta (if non-trivial) if meta is None and data.meta: # At this point do NOT deepcopy data.meta as this will happen after # table init_func() is called. But for table input the table meta # gets a key copy here if copy=False because later a direct object ref # is used. meta = data.meta if copy else data.meta.copy() # Handle indices on input table. Copy primary key and don't copy indices # if the input Table is in non-copy mode. self.primary_key = data.primary_key self._init_indices = self._init_indices and data._copy_indices # Extract default names, n_cols, and then overwrite ``data`` to be the # table columns so we can use _init_from_list. default_names = data.colnames n_cols = len(default_names) data = list(data.columns.values()) init_func = self._init_from_list elif data is None: if names is None: if dtype is None: # Table was initialized as `t = Table()`. Set up for empty # table with names=[], data=[], and n_cols=0. # self._init_from_list() will simply return, giving the # expected empty table. names = [] else: try: # No data nor names but dtype is available. This must be # valid to initialize a structured array. dtype = np.dtype(dtype) names = dtype.names dtype = [dtype[name] for name in names] except Exception: raise ValueError( "dtype was specified but could not be " "parsed for column names" ) # names is guaranteed to be set at this point init_func = self._init_from_list n_cols = len(names) data = [[]] * n_cols else: raise ValueError(f"Data type {type(data)} not allowed to init Table") # Set up defaults if names and/or dtype are not specified. # A value of None means the actual value will be inferred # within the appropriate initialization routine, either from # existing specification or auto-generated. if dtype is None: dtype = [None] * n_cols elif isinstance(dtype, np.dtype): if default_names is None: default_names = dtype.names # Convert a numpy dtype input to a list of dtypes for later use. dtype = [dtype[name] for name in dtype.names] if names is None: names = default_names or [None] * n_cols names = [None if name is None else str(name) for name in names] self._check_names_dtype(names, dtype, n_cols) # Finally do the real initialization init_func(data, names, dtype, n_cols, copy) # Set table meta. If copy=True then deepcopy meta otherwise use the # user-supplied meta directly. if meta is not None: self.meta = deepcopy(meta) if copy else meta # Update meta with TableAttributes supplied as kwargs in Table init. # This takes precedence over previously-defined meta. if meta_table_attrs: for attr, value in meta_table_attrs.items(): setattr(self, attr, value) # Whatever happens above, the masked property should be set to a boolean if self.masked not in (None, True, False): raise TypeError("masked property must be None, True or False") self._set_column_attribute("unit", units) self._set_column_attribute("description", descriptions) def _set_column_attribute(self, attr, values): """Set ``attr`` for columns to ``values``, which can be either a dict (keyed by column name) or a dict of name: value pairs. This is used for handling the ``units`` and ``descriptions`` kwargs to ``__init__``. """ if not values: return if isinstance(values, Row): # For a Row object transform to an equivalent dict. values = {name: values[name] for name in values.colnames} if not isinstance(values, Mapping): # If not a dict map, assume iterable and map to dict if the right length if len(values) != len(self.columns): raise ValueError( f"sequence of {attr} values must match number of columns" ) values = dict(zip(self.colnames, values)) for name, value in values.items(): if name not in self.columns: raise ValueError( f"invalid column name {name} for setting {attr} attribute" ) # Special case: ignore unit if it is an empty or blank string if attr == "unit" and isinstance(value, str): if value.strip() == "": value = None if value is not None and value is not np.ma.masked: col = self[name] if attr == "unit" and isinstance(col, Quantity): # Update the Quantity unit in-place col <<= value else: setattr(col.info, attr, value) def __getstate__(self): columns = OrderedDict( (key, col if isinstance(col, BaseColumn) else col_copy(col)) for key, col in self.columns.items() ) return (columns, self.meta) def __setstate__(self, state): columns, meta = state self.__init__(columns, meta=meta) @property def mask(self): # Dynamic view of available masks if self.masked or self.has_masked_columns or self.has_masked_values: mask_table = Table( [ getattr(col, "mask", FalseArray(col.shape)) for col in self.itercols() ], names=self.colnames, copy=False, ) # Set hidden attribute to force inplace setitem so that code like # t.mask['a'] = [1, 0, 1] will correctly set the underlying mask. # See #5556 for discussion. mask_table._setitem_inplace = True else: mask_table = None return mask_table @mask.setter def mask(self, val): self.mask[:] = val @property def _mask(self): """This is needed so that comparison of a masked Table and a MaskedArray works. The requirement comes from numpy.ma.core so don't remove this property. """ return self.as_array().mask
[docs] def filled(self, fill_value=None): """Return copy of self, with masked values filled. If input ``fill_value`` supplied then that value is used for all masked entries in the table. Otherwise the individual ``fill_value`` defined for each table column is used. Parameters ---------- fill_value : str If supplied, this ``fill_value`` is used for all masked entries in the entire table. Returns ------- filled_table : `~astropy.table.Table` New table with masked values filled """ if self.masked or self.has_masked_columns or self.has_masked_values: # Get new columns with masked values filled, then create Table with those # new cols (copy=False) but deepcopy the meta. data = [ col.filled(fill_value) if hasattr(col, "filled") else col for col in self.itercols() ] return self.__class__(data, meta=deepcopy(self.meta), copy=False) else: # Return copy of the original object. return self.copy()
@property def indices(self): """ Return the indices associated with columns of the table as a TableIndices object. """ lst = [] for column in self.columns.values(): for index in column.info.indices: if sum(index is x for x in lst) == 0: # ensure uniqueness lst.append(index) return TableIndices(lst) @property def loc(self): """ Return a TableLoc object that can be used for retrieving rows by index in a given data range. Note that both loc and iloc work only with single-column indices. """ return TableLoc(self) @property def loc_indices(self): """ Return a TableLocIndices object that can be used for retrieving the row indices corresponding to given table index key value or values. """ return TableLocIndices(self) @property def iloc(self): """ Return a TableILoc object that can be used for retrieving indexed rows in the order they appear in the index. """ return TableILoc(self)
[docs] def add_index(self, colnames, engine=None, unique=False): """ Insert a new index among one or more columns. If there are no indices, make this index the primary table index. Parameters ---------- colnames : str or list List of column names (or a single column name) to index engine : type or None Indexing engine class to use, either `~astropy.table.SortedArray`, `~astropy.table.BST`, or `~astropy.table.SCEngine`. If the supplied argument is None (by default), use `~astropy.table.SortedArray`. unique : bool Whether the values of the index must be unique. Default is False. """ if isinstance(colnames, str): colnames = (colnames,) columns = self.columns[tuple(colnames)].values() # make sure all columns support indexing for col in columns: if not getattr(col.info, "_supports_indexing", False): raise ValueError( f'Cannot create an index on column "{col.info.name}", ' f'of type "{type(col)}"' ) is_primary = not self.indices index = Index(columns, engine=engine, unique=unique) sliced_index = SlicedIndex(index, slice(0, 0, None), original=True) if is_primary: self.primary_key = colnames for col in columns: col.info.indices.append(sliced_index)
[docs] def remove_indices(self, colname): """ Remove all indices involving the given column. If the primary index is removed, the new primary index will be the most recently added remaining index. Parameters ---------- colname : str Name of column """ col = self.columns[colname] for index in self.indices: try: index.col_position(col.info.name) except ValueError: pass else: for c in index.columns: c.info.indices.remove(index)
[docs] def index_mode(self, mode): """ Return a context manager for an indexing mode. Parameters ---------- mode : str Either 'freeze', 'copy_on_getitem', or 'discard_on_copy'. In 'discard_on_copy' mode, indices are not copied whenever columns or tables are copied. In 'freeze' mode, indices are not modified whenever columns are modified; at the exit of the context, indices refresh themselves based on column values. This mode is intended for scenarios in which one intends to make many additions or modifications in an indexed column. In 'copy_on_getitem' mode, indices are copied when taking column slices as well as table slices, so col[i0:i1] will preserve indices. """ return _IndexModeContext(self, mode)
def __array__(self, dtype=None, copy=COPY_IF_NEEDED): """Support converting Table to np.array via np.array(table). Coercion to a different dtype via np.array(table, dtype) is not supported and will raise a ValueError. """ if dtype is not None: if np.dtype(dtype) != object: raise ValueError("Datatype coercion is not allowed") out = np.array(None, dtype=object, copy=copy) out[()] = self return out # This limitation is because of the following unexpected result that # should have made a table copy while changing the column names. # # >>> d = astropy.table.Table([[1,2],[3,4]]) # >>> np.array(d, dtype=[('a', 'i8'), ('b', 'i8')]) # array([(0, 0), (0, 0)], # dtype=[('a', '<i8'), ('b', '<i8')]) out = self.as_array() return out.data if isinstance(out, np.ma.MaskedArray) else out def _check_names_dtype(self, names, dtype, n_cols): """Make sure that names and dtype are both iterable and have the same length as data. """ for inp_list, inp_str in ((dtype, "dtype"), (names, "names")): if not isiterable(inp_list): raise ValueError(f"{inp_str} must be a list or None") if len(names) != n_cols or len(dtype) != n_cols: raise ValueError( 'Arguments "names" and "dtype" must match number of columns' ) def _init_from_list_of_dicts(self, data, names, dtype, n_cols, copy): """Initialize table from a list of dictionaries representing rows.""" # Define placeholder for missing values as a unique object that cannot # every occur in user data. MISSING = object() # Gather column names that exist in the input `data`. names_from_data = set() for row in data: names_from_data.update(row) if set(data[0].keys()) == names_from_data: names_from_data = list(data[0].keys()) else: names_from_data = sorted(names_from_data) # Note: if set(data[0].keys()) != names_from_data, this will give an # exception later, so NO need to catch here. # Convert list of dict into dict of list (cols), keep track of missing # indexes and put in MISSING placeholders in the `cols` lists. cols = {} missing_indexes = defaultdict(list) for name in names_from_data: cols[name] = [] for ii, row in enumerate(data): try: val = row[name] except KeyError: missing_indexes[name].append(ii) val = MISSING cols[name].append(val) # Fill the missing entries with first values if missing_indexes: for name, indexes in missing_indexes.items(): col = cols[name] first_val = next(val for val in col if val is not MISSING) for index in indexes: col[index] = first_val # prepare initialization if all(name is None for name in names): names = names_from_data self._init_from_dict(cols, names, dtype, n_cols, copy) # Mask the missing values if necessary, converting columns to MaskedColumn # as needed. if missing_indexes: for name, indexes in missing_indexes.items(): col = self[name] # Ensure that any Column subclasses with MISSING values can support # setting masked values. As of astropy 4.0 the test condition below is # always True since _init_from_dict cannot result in mixin columns. if isinstance(col, Column) and not isinstance(col, MaskedColumn): self[name] = self.MaskedColumn(col, copy=False) # Finally do the masking in a mixin-safe way. self[name][indexes] = np.ma.masked def _init_from_list(self, data, names, dtype, n_cols, copy): """Initialize table from a list of column data. A column can be a Column object, np.ndarray, mixin, or any other iterable object. """ # Special case of initializing an empty table like `t = Table()`. No # action required at this point. if n_cols == 0: return cols = [] default_names = _auto_names(n_cols) for col, name, default_name, dt in zip(data, names, default_names, dtype): col = self._convert_data_to_col(col, copy, default_name, dt, name) cols.append(col) self._init_from_cols(cols) def _convert_data_to_col( self, data, copy=True, default_name=None, dtype=None, name=None ): """ Convert any allowed sequence data ``col`` to a column object that can be used directly in the self.columns dict. This could be a Column, MaskedColumn, or mixin column. The final column name is determined by:: name or data.info.name or def_name If ``data`` has no ``info`` then ``name = name or def_name``. The behavior of ``copy`` for Column objects is: - copy=True: new class instance with a copy of data and deep copy of meta - copy=False: new class instance with same data and a key-only copy of meta For mixin columns: - copy=True: new class instance with copy of data and deep copy of meta - copy=False: original instance (no copy at all) Parameters ---------- data : object (column-like sequence) Input column data copy : bool Make a copy default_name : str Default name dtype : np.dtype or None Data dtype name : str or None Column name Returns ------- col : Column, MaskedColumn, mixin-column type Object that can be used as a column in self """ data_is_mixin = self._is_mixin_for_table(data) masked_col_cls = ( self.ColumnClass if issubclass(self.ColumnClass, self.MaskedColumn) else self.MaskedColumn ) try: data0_is_mixin = self._is_mixin_for_table(data[0]) except Exception: # Need broad exception, cannot predict what data[0] raises for arbitrary data data0_is_mixin = False # If the data is not an instance of Column or a mixin class, we can # check the registry of mixin 'handlers' to see if the column can be # converted to a mixin class if (handler := get_mixin_handler(data)) is not None: original_data = data data = handler(data) if not (data_is_mixin := self._is_mixin_for_table(data)): fully_qualified_name = ( original_data.__class__.__module__ + "." + original_data.__class__.__name__ ) raise TypeError( "Mixin handler for object of type " f"{fully_qualified_name} " "did not return a valid mixin column" ) # Get the final column name using precedence. Some objects may not # have an info attribute. Also avoid creating info as a side effect. if not name: if isinstance(data, Column): name = data.name or default_name elif "info" in getattr(data, "__dict__", ()): name = data.info.name or default_name else: name = default_name if isinstance(data, Column): # If self.ColumnClass is a subclass of col, then "upgrade" to ColumnClass, # otherwise just use the original class. The most common case is a # table with masked=True and ColumnClass=MaskedColumn. Then a Column # gets upgraded to MaskedColumn, but the converse (pre-4.0) behavior # of downgrading from MaskedColumn to Column (for non-masked table) # does not happen. col_cls = self._get_col_cls_for_table(data) elif data_is_mixin: # Copy the mixin column attributes if they exist since the copy below # may not get this attribute. If not copying, take a slice # to ensure we get a new instance and we do not share metadata # like info. col = col_copy(data, copy_indices=self._init_indices) if copy else data[:] col.info.name = name return col elif data0_is_mixin: # Handle case of a sequence of a mixin, e.g. [1*u.m, 2*u.m]. try: col = data[0].__class__(data) col.info.name = name return col except Exception: # If that didn't work for some reason, just turn it into np.array of object data = np.array(data, dtype=object) col_cls = self.ColumnClass elif isinstance(data, (np.ma.MaskedArray, Masked)): # Require that col_cls be a subclass of MaskedColumn, remembering # that ColumnClass could be a user-defined subclass (though more-likely # could be MaskedColumn). col_cls = masked_col_cls elif data is None: # Special case for data passed as the None object (for broadcasting # to an object column). Need to turn data into numpy `None` scalar # object, otherwise `Column` interprets data=None as no data instead # of a object column of `None`. data = np.array(None) col_cls = self.ColumnClass elif not hasattr(data, "dtype"): # `data` is none of the above, convert to numpy array or MaskedArray # assuming only that it is a scalar or sequence or N-d nested # sequence. This function is relatively intricate and tries to # maintain performance for common cases while handling things like # list input with embedded np.ma.masked entries. If `data` is a # scalar then it gets returned unchanged so the original object gets # passed to `Column` later. data = _convert_sequence_data_to_array(data, dtype) copy = COPY_IF_NEEDED # Already made a copy above col_cls = ( masked_col_cls if isinstance(data, np.ma.MaskedArray) else self.ColumnClass ) else: col_cls = self.ColumnClass try: col = col_cls( name=name, data=data, dtype=dtype, copy=copy, copy_indices=self._init_indices, ) except Exception: # Broad exception class since we don't know what might go wrong raise ValueError("unable to convert data to Column for Table") col = self._convert_col_for_table(col) return col def _init_from_ndarray(self, data, names, dtype, n_cols, copy): """Initialize table from an ndarray structured array.""" data_names = data.dtype.names or _auto_names(n_cols) struct = data.dtype.names is not None names = [name or data_names[i] for i, name in enumerate(names)] cols = ( [data[name] for name in data_names] if struct else [data[:, i] for i in range(n_cols)] ) self._init_from_list(cols, names, dtype, n_cols, copy) def _init_from_dict(self, data, names, dtype, n_cols, copy): """Initialize table from a dictionary of columns.""" data_list = [data[name] for name in names] self._init_from_list(data_list, names, dtype, n_cols, copy) def _get_col_cls_for_table(self, col): """Get the correct column class to use for upgrading any Column-like object. For a masked table, ensure any Column-like object is a subclass of the table MaskedColumn. For unmasked table, ensure any MaskedColumn-like object is a subclass of the table MaskedColumn. If not a MaskedColumn, then ensure that any Column-like object is a subclass of the table Column. """ col_cls = col.__class__ if self.masked: if isinstance(col, Column) and not isinstance(col, self.MaskedColumn): col_cls = self.MaskedColumn else: if isinstance(col, MaskedColumn): if not isinstance(col, self.MaskedColumn): col_cls = self.MaskedColumn elif isinstance(col, Column) and not isinstance(col, self.Column): col_cls = self.Column return col_cls def _convert_col_for_table(self, col): """ Make sure that all Column objects have correct base class for this type of Table. For a base Table this most commonly means setting to MaskedColumn if the table is masked. Table subclasses like QTable override this method. """ if isinstance(col, Column) and not isinstance(col, self.ColumnClass): col_cls = self._get_col_cls_for_table(col) if col_cls is not col.__class__: col = col_cls(col, copy=COPY_IF_NEEDED) return col def _init_from_cols(self, cols): """Initialize table from a list of Column or mixin objects.""" lengths = {len(col) for col in cols} if len(lengths) > 1: raise ValueError(f"Inconsistent data column lengths: {lengths}") # Make sure that all Column-based objects have correct class. For # plain Table this is self.ColumnClass, but for instance QTable will # convert columns with units to a Quantity mixin. newcols = [self._convert_col_for_table(col) for col in cols] self._make_table_from_cols(self, newcols) # Deduplicate indices. It may happen that after pickling or when # initing from an existing table that column indices which had been # references to a single index object got *copied* into an independent # object. This results in duplicates which will cause downstream problems. index_dict = {} for col in self.itercols(): for i, index in enumerate(col.info.indices or []): names = tuple(ind_col.info.name for ind_col in index.columns) if names in index_dict: col.info.indices[i] = index_dict[names] else: index_dict[names] = index def _new_from_slice(self, slice_): """Create a new table as a referenced slice from self.""" table = self.__class__(masked=self.masked) if self.meta: table.meta = self.meta.copy() # Shallow copy for slice table.primary_key = self.primary_key newcols = [] for col in self.columns.values(): newcol = col[slice_] # Note in line below, use direct attribute access to col.indices for Column # instances instead of the generic col.info.indices. This saves about 4 usec # per column. if (col if isinstance(col, Column) else col.info).indices: # TODO : as far as I can tell the only purpose of setting _copy_indices # here is to communicate that to the initial test in `slice_indices`. # Why isn't that just sent as an arg to the function? col.info._copy_indices = self._copy_indices newcol = col.info.slice_indices(newcol, slice_, len(col)) # Don't understand why this is forcing a value on the original column. # Normally col.info does not even have a _copy_indices attribute. Tests # still pass if this line is deleted. (Each col.info attribute access # is expensive). col.info._copy_indices = True newcols.append(newcol) self._make_table_from_cols( table, newcols, verify=False, names=self.columns.keys() ) return table @staticmethod def _make_table_from_cols(table, cols, verify=True, names=None): """ Make ``table`` in-place so that it represents the given list of ``cols``. """ if names is None: names = [col.info.name for col in cols] # Note: we do not test for len(names) == len(cols) if names is not None. In that # case the function is being called by from "trusted" source (e.g. right above here) # that is assumed to provide valid inputs. In that case verify=False. if verify: if None in names: raise TypeError("Cannot have None for column name") if len(set(names)) != len(names): raise ValueError("Duplicate column names") table.columns = table.TableColumns( (name, col) for name, col in zip(names, cols) ) for col in cols: table._set_col_parent_table_and_mask(col) def _set_col_parent_table_and_mask(self, col): """ Set ``col.parent_table = self`` and force ``col`` to have ``mask`` attribute if the table is masked and ``col.mask`` does not exist. """ # For Column instances it is much faster to do direct attribute access # instead of going through .info col_info = col if isinstance(col, Column) else col.info col_info.parent_table = self # Legacy behavior for masked table if self.masked and not hasattr(col, "mask"): col.mask = FalseArray(col.shape)
[docs] def itercols(self): """ Iterate over the columns of this table. Examples -------- To iterate over the columns of a table:: >>> t = Table([[1], [2]]) >>> for col in t.itercols(): ... print(col) col0 ---- 1 col1 ---- 2 Using ``itercols()`` is similar to ``for col in t.columns.values()`` but is syntactically preferred. """ for colname in self.columns: yield self[colname]
def _base_repr_( self, html=False, descr_vals=None, max_width=None, tableid=None, show_dtype=True, max_lines=None, tableclass=None, ): if descr_vals is None: descr_vals = [self.__class__.__name__] if self.masked: descr_vals.append("masked=True") descr_vals.append(f"length={len(self)}") descr = " ".join(descr_vals) if html: from astropy.utils.xml.writer import xml_escape descr = f"<i>{xml_escape(descr)}</i>\n" else: descr = f"<{descr}>\n" if tableid is None: tableid = f"table{id(self)}" data_lines, outs = self.formatter._pformat_table( self, tableid=tableid, html=html, max_width=max_width, show_name=True, show_unit=None, show_dtype=show_dtype, max_lines=max_lines, tableclass=tableclass, ) out = descr + "\n".join(data_lines) return out def _repr_html_(self): out = self._base_repr_( html=True, max_width=-1, tableclass=conf.default_notebook_table_class ) # Wrap <table> in <div>. This follows the pattern in pandas and allows # table to be scrollable horizontally in VS Code notebook display. out = f"<div>{out}</div>" return out def __repr__(self): return self._base_repr_(html=False, max_width=None) def __str__(self): return "\n".join(self.pformat()) def __bytes__(self): return str(self).encode("utf-8") @property def has_mixin_columns(self): """ True if table has any mixin columns (defined as columns that are not Column subclasses). """ return any(has_info_class(col, MixinInfo) for col in self.columns.values()) @property def has_masked_columns(self): """True if table has any ``MaskedColumn`` columns. This does not check for mixin columns that may have masked values, use the ``has_masked_values`` property in that case. """ return any(isinstance(col, MaskedColumn) for col in self.itercols()) @property def has_masked_values(self): """True if column in the table has values which are masked. This may be relatively slow for large tables as it requires checking the mask values of each column. """ return any( hasattr(col, "mask") and np.any(col.mask != np.zeros((), col.mask.dtype)) for col in self.itercols() ) def _is_mixin_for_table(self, col): """ Determine if ``col`` should be added to the table directly as a mixin column. """ if isinstance(col, BaseColumn): return False # Is it a mixin but not [Masked]Quantity (which gets converted to # [Masked]Column with unit set). return has_info_class(col, MixinInfo) and not has_info_class(col, QuantityInfo)
[docs] @format_doc(_pprint_docs) def pprint( self, max_lines=None, max_width=None, show_name=True, show_unit=None, show_dtype=False, align=None, ): """Print a formatted string representation of the table. If no value of ``max_lines`` is supplied then the height of the screen terminal is used to set ``max_lines``. If the terminal height cannot be determined then the default is taken from the configuration item ``astropy.conf.max_lines``. If a negative value of ``max_lines`` is supplied then there is no line limit applied. The same applies for max_width except the configuration item is ``astropy.conf.max_width``. """ lines, outs = self.formatter._pformat_table( self, max_lines, max_width, show_name=show_name, show_unit=show_unit, show_dtype=show_dtype, align=align, ) if outs["show_length"]: lines.append(f"Length = {len(self)} rows") n_header = outs["n_header"] for i, line in enumerate(lines): if i < n_header: color_print(line, "red") else: print(line)
[docs] @format_doc(_pprint_docs) def pprint_all( self, max_lines=-1, max_width=-1, show_name=True, show_unit=None, show_dtype=False, align=None, ): """Print a formatted string representation of the entire table. This method is the same as `astropy.table.Table.pprint` except that the default ``max_lines`` and ``max_width`` are both -1 so that by default the entire table is printed instead of restricting to the size of the screen terminal. """ return self.pprint( max_lines, max_width, show_name, show_unit, show_dtype, align )
def _make_index_row_display_table(self, index_row_name): if index_row_name not in self.columns: idx_col = self.ColumnClass(name=index_row_name, data=np.arange(len(self))) return self.__class__([idx_col] + list(self.columns.values()), copy=False) else: return self
[docs] @deprecated( "6.1", message="""show_in_notebook() is deprecated as of 6.1 and to create interactive tables it is recommended to use dedicated tools like: - https://github.com/bloomberg/ipydatagrid - https://docs.bokeh.org/en/latest/docs/user_guide/interaction/widgets.html#datatable - https://dash.plotly.com/datatable""", ) def show_in_notebook( self, tableid=None, css=None, display_length=50, table_class="astropy-default", show_row_index="idx", ): """Render the table in HTML and show it in the IPython notebook. Parameters ---------- tableid : str or None An html ID tag for the table. Default is ``table{id}-XXX``, where id is the unique integer id of the table object, id(self), and XXX is a random number to avoid conflicts when printing the same table multiple times. table_class : str or None A string with a list of HTML classes used to style the table. The special default string ('astropy-default') means that the string will be retrieved from the configuration item ``astropy.table.default_notebook_table_class``. Note that these table classes may make use of bootstrap, as this is loaded with the notebook. See `this page <https://getbootstrap.com/css/#tables>`_ for the list of classes. css : str A valid CSS string declaring the formatting for the table. Defaults to ``astropy.table.jsviewer.DEFAULT_CSS_NB``. display_length : int, optional Number or rows to show. Defaults to 50. show_row_index : str or False If this does not evaluate to False, a column with the given name will be added to the version of the table that gets displayed. This new column shows the index of the row in the table itself, even when the displayed table is re-sorted by another column. Note that if a column with this name already exists, this option will be ignored. Defaults to "idx". Notes ----- Currently, unlike `show_in_browser` (with ``jsviewer=True``), this method needs to access online javascript code repositories. This is due to modern browsers' limitations on accessing local files. Hence, if you call this method while offline (and don't have a cached version of jquery and jquery.dataTables), you will not get the jsviewer features. """ from IPython.display import HTML from .jsviewer import JSViewer if tableid is None: tableid = f"table{id(self)}-{np.random.randint(1, 1e6)}" jsv = JSViewer(display_length=display_length) if show_row_index: display_table = self._make_index_row_display_table(show_row_index) else: display_table = self if table_class == "astropy-default": table_class = conf.default_notebook_table_class html = display_table._base_repr_( html=True, max_width=-1, tableid=tableid, max_lines=-1, show_dtype=False, tableclass=table_class, ) columns = display_table.columns.values() sortable_columns = [ i for i, col in enumerate(columns) if col.info.dtype.kind in "iufc" ] html += jsv.ipynb(tableid, css=css, sort_columns=sortable_columns) return HTML(html)
[docs] @deprecated( "6.1", pending=True, message="""We are planning on deprecating show_in_browser in the future. If you are actively using this method, please let us know at https://github.com/astropy/astropy/issues/16067""", ) def show_in_browser( self, max_lines=5000, jsviewer=False, browser="default", jskwargs={"use_local_files": True}, tableid=None, table_class="display compact", css=None, show_row_index="idx", ): """Render the table in HTML and show it in a web browser. Parameters ---------- max_lines : int Maximum number of rows to export to the table (set low by default to avoid memory issues, since the browser view requires duplicating the table in memory). A negative value of ``max_lines`` indicates no row limit. jsviewer : bool If `True`, prepends some javascript headers so that the table is rendered as a `DataTables <https://datatables.net>`_ data table. This allows in-browser searching & sorting. browser : str Any legal browser name, e.g. ``'firefox'``, ``'chrome'``, ``'safari'`` (for mac, you may need to use ``'open -a "/Applications/Google Chrome.app" {}'`` for Chrome). If ``'default'``, will use the system default browser. jskwargs : dict Passed to the `astropy.table.JSViewer` init. Defaults to ``{'use_local_files': True}`` which means that the JavaScript libraries will be served from local copies. tableid : str or None An html ID tag for the table. Default is ``table{id}``, where id is the unique integer id of the table object, id(self). table_class : str or None A string with a list of HTML classes used to style the table. Default is "display compact", and other possible values can be found in https://www.datatables.net/manual/styling/classes css : str A valid CSS string declaring the formatting for the table. Defaults to ``astropy.table.jsviewer.DEFAULT_CSS``. show_row_index : str or False If this does not evaluate to False, a column with the given name will be added to the version of the table that gets displayed. This new column shows the index of the row in the table itself, even when the displayed table is re-sorted by another column. Note that if a column with this name already exists, this option will be ignored. Defaults to "idx". """ import os import tempfile import webbrowser from urllib.parse import urljoin from urllib.request import pathname2url from .jsviewer import DEFAULT_CSS if css is None: css = DEFAULT_CSS # We can't use NamedTemporaryFile here because it gets deleted as # soon as it gets garbage collected. tmpdir = tempfile.mkdtemp() path = os.path.join(tmpdir, "table.html") with open(path, "w") as tmp: if jsviewer: if show_row_index: display_table = self._make_index_row_display_table(show_row_index) else: display_table = self display_table.write( tmp, format="jsviewer", css=css, max_lines=max_lines, jskwargs=jskwargs, table_id=tableid, table_class=table_class, ) else: self.write(tmp, format="html") try: br = webbrowser.get(None if browser == "default" else browser) except webbrowser.Error: log.error(f"Browser '{browser}' not found.") else: br.open(urljoin("file:", pathname2url(path)))
[docs] @format_doc(_pformat_docs, id="{id}") def pformat( self, max_lines=None, max_width=None, show_name=True, show_unit=None, show_dtype=False, html=False, tableid=None, align=None, tableclass=None, ): """Return a list of lines for the formatted string representation of the table. If no value of ``max_lines`` is supplied then the height of the screen terminal is used to set ``max_lines``. If the terminal height cannot be determined then the default is taken from the configuration item ``astropy.conf.max_lines``. If a negative value of ``max_lines`` is supplied then there is no line limit applied. The same applies for ``max_width`` except the configuration item is ``astropy.conf.max_width``. """ lines, outs = self.formatter._pformat_table( self, max_lines, max_width, show_name=show_name, show_unit=show_unit, show_dtype=show_dtype, html=html, tableid=tableid, tableclass=tableclass, align=align, ) if outs["show_length"]: lines.append(f"Length = {len(self)} rows") return lines
[docs] @format_doc(_pformat_docs, id="{id}") def pformat_all( self, max_lines=-1, max_width=-1, show_name=True, show_unit=None, show_dtype=False, html=False, tableid=None, align=None, tableclass=None, ): """Return a list of lines for the formatted string representation of the entire table. If no value of ``max_lines`` is supplied then the height of the screen terminal is used to set ``max_lines``. If the terminal height cannot be determined then the default is taken from the configuration item ``astropy.conf.max_lines``. If a negative value of ``max_lines`` is supplied then there is no line limit applied. The same applies for ``max_width`` except the configuration item is ``astropy.conf.max_width``. """ return self.pformat( max_lines, max_width, show_name, show_unit, show_dtype, html, tableid, align, tableclass, )
[docs] def more( self, max_lines=None, max_width=None, show_name=True, show_unit=None, show_dtype=False, ): """Interactively browse table with a paging interface. Supported keys:: f, <space> : forward one page b : back one page r : refresh same page n : next row p : previous row < : go to beginning > : go to end q : quit browsing h : print this help Parameters ---------- max_lines : int Maximum number of lines in table output max_width : int or None Maximum character width of output show_name : bool Include a header row for column names. Default is True. show_unit : bool Include a header row for unit. Default is to show a row for units only if one or more columns has a defined value for the unit. show_dtype : bool Include a header row for column dtypes. Default is False. """ self.formatter._more_tabcol( self, max_lines, max_width, show_name=show_name, show_unit=show_unit, show_dtype=show_dtype, )
def __getitem__(self, item): if isinstance(item, str): return self.columns[item] elif isinstance(item, (int, np.integer)): return self.Row(self, item) elif ( isinstance(item, np.ndarray) and item.shape == () and item.dtype.kind == "i" ): return self.Row(self, item.item()) elif self._is_list_or_tuple_of_str(item): out = self.__class__( [self[x] for x in item], copy_indices=self._copy_indices ) out._groups = groups.TableGroups( out, indices=self.groups._indices, keys=self.groups._keys ) out.meta = self.meta.copy() # Shallow copy for meta return out elif (isinstance(item, np.ndarray) and item.size == 0) or ( isinstance(item, (tuple, list)) and not item ): # If item is an empty array/list/tuple then return the table with no rows return self._new_from_slice([]) elif ( isinstance(item, (slice, np.ndarray, list)) or isinstance(item, tuple) and all(isinstance(x, np.ndarray) for x in item) ): # here for the many ways to give a slice; a tuple of ndarray # is produced by np.where, as in t[np.where(t['a'] > 2)] # For all, a new table is constructed with slice of all columns return self._new_from_slice(item) else: raise ValueError(f"Illegal type {type(item)} for table item access") def __setitem__(self, item, value): # If the item is a string then it must be the name of a column. # If that column doesn't already exist then create it now. if isinstance(item, str) and item not in self.colnames: self.add_column(value, name=item, copy=True) else: n_cols = len(self.columns) if isinstance(item, str): # Set an existing column by first trying to replace, and if # this fails do an in-place update. See definition of mask # property for discussion of the _setitem_inplace attribute. if ( not getattr(self, "_setitem_inplace", False) and not conf.replace_inplace ): try: self._replace_column_warnings(item, value) return except Exception: pass self.columns[item][:] = value elif isinstance(item, (int, np.integer)): self._set_row(idx=item, colnames=self.colnames, vals=value) elif ( isinstance(item, (slice, np.ndarray, list)) or isinstance(item, tuple) and all(isinstance(x, np.ndarray) for x in item) ): if isinstance(value, Table): vals = (col for col in value.columns.values()) elif isinstance(value, np.ndarray) and value.dtype.names: vals = (value[name] for name in value.dtype.names) elif np.isscalar(value): vals = itertools.repeat(value, n_cols) else: # Assume this is an iterable that will work if len(value) != n_cols: raise ValueError( f"Right side value needs {n_cols} elements (one for each column)" ) vals = value for col, val in zip(self.columns.values(), vals): col[item] = val else: raise ValueError(f"Illegal type {type(item)} for table item access") def __delitem__(self, item): if isinstance(item, str): self.remove_column(item) elif isinstance(item, (int, np.integer)): self.remove_row(item) elif isinstance(item, (list, tuple, np.ndarray)) and all( isinstance(x, str) for x in item ): self.remove_columns(item) elif ( isinstance(item, (list, np.ndarray)) and np.asarray(item).dtype.kind == "i" ): self.remove_rows(item) elif isinstance(item, slice): self.remove_rows(item) else: raise IndexError("illegal key or index value") def _ipython_key_completions_(self): return self.colnames
[docs] def field(self, item): """Return column[item] for recarray compatibility.""" return self.columns[item]
@property def masked(self): return self._masked @masked.setter def masked(self, masked): raise Exception( "Masked attribute is read-only (use t = Table(t, masked=True)" " to convert to a masked table)" ) def _set_masked(self, masked): """ Set the table masked property. Parameters ---------- masked : bool State of table masking (`True` or `False`) """ if masked in [True, False, None]: self._masked = masked else: raise ValueError("masked should be one of True, False, None") self._column_class = self.MaskedColumn if self._masked else self.Column @property def ColumnClass(self): if self._column_class is None: return self.Column else: return self._column_class @property def dtype(self): return np.dtype([descr(col) for col in self.columns.values()]) @property def colnames(self): return list(self.columns.keys()) @staticmethod def _is_list_or_tuple_of_str(names): """Check that ``names`` is a tuple or list of strings.""" return ( isinstance(names, (tuple, list)) and names and all(isinstance(x, str) for x in names) )
[docs] def keys(self): return list(self.columns.keys())
[docs] def values(self): return self.columns.values()
[docs] def items(self): return self.columns.items()
def __len__(self): # For performance reasons (esp. in Row) cache the first column name # and use that subsequently for the table length. If might not be # available yet or the column might be gone now, in which case # try again in the except block. try: return len(OrderedDict.__getitem__(self.columns, self._first_colname)) except (AttributeError, KeyError): if len(self.columns) == 0: return 0 # Get the first column name self._first_colname = next(iter(self.columns)) return len(self.columns[self._first_colname]) def __or__(self, other): if isinstance(other, Table): updated_table = self.copy() updated_table.update(other) return updated_table else: return NotImplemented def __ior__(self, other): try: self.update(other) return self except TypeError: return NotImplemented
[docs] def index_column(self, name): """ Return the positional index of column ``name``. Parameters ---------- name : str column name Returns ------- index : int Positional index of column ``name``. Examples -------- Create a table with three columns 'a', 'b' and 'c':: >>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']], ... names=('a', 'b', 'c')) >>> print(t) a b c --- --- --- 1 0.1 x 2 0.2 y 3 0.3 z Get index of column 'b' of the table:: >>> t.index_column('b') 1 """ try: return self.colnames.index(name) except ValueError: raise ValueError(f"Column {name} does not exist")
[docs] def add_column( self, col, index=None, name=None, rename_duplicate=False, copy=True, default_name=None, ): """ Add a new column to the table using ``col`` as input. If ``index`` is supplied then insert column before ``index`` position in the list of columns, otherwise append column to the end of the list. The ``col`` input can be any data object which is acceptable as a `~astropy.table.Table` column object or can be converted. This includes mixin columns and scalar or length=1 objects which get broadcast to match the table length. To add several columns at once use ``add_columns()`` or simply call ``add_column()`` for each one. There is very little performance difference in the two approaches. Parameters ---------- col : object Data object for the new column index : int or None Insert column before this position or at end (default). name : str Column name rename_duplicate : bool Uniquify column name if it already exist. Default is False. copy : bool Make a copy of the new column. Default is True. default_name : str or None Name to use if both ``name`` and ``col.info.name`` are not available. Defaults to ``col{number_of_columns}``. Examples -------- Create a table with two columns 'a' and 'b', then create a third column 'c' and append it to the end of the table:: >>> t = Table([[1, 2], [0.1, 0.2]], names=('a', 'b')) >>> col_c = Column(name='c', data=['x', 'y']) >>> t.add_column(col_c) >>> print(t) a b c --- --- --- 1 0.1 x 2 0.2 y Add column 'd' at position 1. Note that the column is inserted before the given index:: >>> t.add_column(['a', 'b'], name='d', index=1) >>> print(t) a d b c --- --- --- --- 1 a 0.1 x 2 b 0.2 y Add second column named 'b' with rename_duplicate:: >>> t = Table([[1, 2], [0.1, 0.2]], names=('a', 'b')) >>> t.add_column(1.1, name='b', rename_duplicate=True) >>> print(t) a b b_1 --- --- --- 1 0.1 1.1 2 0.2 1.1 Add an unnamed column or mixin object in the table using a default name or by specifying an explicit name with ``name``. Name can also be overridden:: >>> t = Table([[1, 2], [0.1, 0.2]], names=('a', 'b')) >>> t.add_column(['a', 'b']) >>> t.add_column(col_c, name='d') >>> print(t) a b col2 d --- --- ---- --- 1 0.1 a x 2 0.2 b y """ if default_name is None: default_name = f"col{len(self.columns)}" # Convert col data to acceptable object for insertion into self.columns. # Note that along with the lines above and below, this allows broadcasting # of scalars to the correct shape for adding to table. col = self._convert_data_to_col( col, name=name, copy=copy, default_name=default_name ) # Assigning a scalar column to an empty table should result in an # exception (see #3811). if col.shape == () and len(self) == 0: raise TypeError("Empty table cannot have column set to scalar value") # Make col data shape correct for scalars. The second test is to allow # broadcasting an N-d element to a column, e.g. t['new'] = [[1, 2]]. elif (col.shape == () or col.shape[0] == 1) and len(self) > 0: new_shape = (len(self),) + getattr(col, "shape", ())[1:] if isinstance(col, np.ndarray): col = np.broadcast_to(col, shape=new_shape, subok=True) elif isinstance(col, ShapedLikeNDArray): col = col._apply(np.broadcast_to, shape=new_shape, subok=True) # broadcast_to() results in a read-only array. Apparently it only changes # the view to look like the broadcasted array. So copy. col = col_copy(col) name = col.info.name # Ensure that new column is the right length if len(self.columns) > 0 and len(col) != len(self): raise ValueError("Inconsistent data column lengths") if rename_duplicate: orig_name = name i = 1 while name in self.columns: # Iterate until a unique name is found name = orig_name + "_" + str(i) i += 1 col.info.name = name # Set col parent_table weakref and ensure col has mask attribute if table.masked self._set_col_parent_table_and_mask(col) # Add new column as last column self.columns[name] = col if index is not None: # Move the other cols to the right of the new one move_names = self.colnames[index:-1] for move_name in move_names: self.columns.move_to_end(move_name, last=True)
[docs] def add_columns( self, cols, indexes=None, names=None, copy=True, rename_duplicate=False ): """ Add a list of new columns the table using ``cols`` data objects. If a corresponding list of ``indexes`` is supplied then insert column before each ``index`` position in the *original* list of columns, otherwise append columns to the end of the list. The ``cols`` input can include any data objects which are acceptable as `~astropy.table.Table` column objects or can be converted. This includes mixin columns and scalar or length=1 objects which get broadcast to match the table length. From a performance perspective there is little difference between calling this method once or looping over the new columns and calling ``add_column()`` for each column. Parameters ---------- cols : list of object List of data objects for the new columns indexes : list of int or None Insert column before this position or at end (default). names : list of str Column names copy : bool Make a copy of the new columns. Default is True. rename_duplicate : bool Uniquify new column names if they duplicate the existing ones. Default is False. See Also -------- astropy.table.hstack, update, replace_column Examples -------- Create a table with two columns 'a' and 'b', then create columns 'c' and 'd' and append them to the end of the table:: >>> t = Table([[1, 2], [0.1, 0.2]], names=('a', 'b')) >>> col_c = Column(name='c', data=['x', 'y']) >>> col_d = Column(name='d', data=['u', 'v']) >>> t.add_columns([col_c, col_d]) >>> print(t) a b c d --- --- --- --- 1 0.1 x u 2 0.2 y v Add column 'c' at position 0 and column 'd' at position 1. Note that the columns are inserted before the given position:: >>> t = Table([[1, 2], [0.1, 0.2]], names=('a', 'b')) >>> t.add_columns([['x', 'y'], ['u', 'v']], names=['c', 'd'], ... indexes=[0, 1]) >>> print(t) c a d b --- --- --- --- x 1 u 0.1 y 2 v 0.2 Add second column 'b' and column 'c' with ``rename_duplicate``:: >>> t = Table([[1, 2], [0.1, 0.2]], names=('a', 'b')) >>> t.add_columns([[1.1, 1.2], ['x', 'y']], names=('b', 'c'), ... rename_duplicate=True) >>> print(t) a b b_1 c --- --- --- --- 1 0.1 1.1 x 2 0.2 1.2 y Add unnamed columns or mixin objects in the table using default names or by specifying explicit names with ``names``. Names can also be overridden:: >>> t = Table() >>> col_b = Column(name='b', data=['u', 'v']) >>> t.add_columns([[1, 2], col_b]) >>> t.add_columns([[3, 4], col_b], names=['c', 'd']) >>> print(t) col0 b c d ---- --- --- --- 1 u 3 u 2 v 4 v """ if indexes is None: indexes = [len(self.columns)] * len(cols) elif len(indexes) != len(cols): raise ValueError("Number of indexes must match number of cols") if names is None: names = (None,) * len(cols) elif len(names) != len(cols): raise ValueError("Number of names must match number of cols") default_names = [f"col{ii + len(self.columns)}" for ii in range(len(cols))] for ii in reversed(np.argsort(indexes, kind="stable")): self.add_column( cols[ii], index=indexes[ii], name=names[ii], default_name=default_names[ii], rename_duplicate=rename_duplicate, copy=copy, )
def _replace_column_warnings(self, name, col): """ Same as replace_column but issues warnings under various circumstances. """ warns = conf.replace_warnings refcount = None old_col = None # sys.getrefcount is CPython specific and not on PyPy. if ( "refcount" in warns and name in self.colnames and hasattr(sys, "getrefcount") ): refcount = sys.getrefcount(self[name]) if name in self.colnames: old_col = self[name] # This may raise an exception (e.g. t['a'] = 1) in which case none of # the downstream code runs. self.replace_column(name, col) if "always" in warns: warnings.warn( f"replaced column '{name}'", TableReplaceWarning, stacklevel=3 ) if "slice" in warns: try: # Check for ndarray-subclass slice. An unsliced instance # has an ndarray for the base while sliced has the same class # as parent. if isinstance(old_col.base, old_col.__class__): msg = ( f"replaced column '{name}' which looks like an array slice. " "The new column no longer shares memory with the " "original array." ) warnings.warn(msg, TableReplaceWarning, stacklevel=3) except AttributeError: pass # sys.getrefcount is CPython specific and not on PyPy. if "refcount" in warns and hasattr(sys, "getrefcount"): # Did reference count change? new_refcount = sys.getrefcount(self[name]) if refcount != new_refcount: msg = ( f"replaced column '{name}' and the number of references " "to the column changed." ) warnings.warn(msg, TableReplaceWarning, stacklevel=3) if "attributes" in warns: # Any of the standard column attributes changed? changed_attrs = [] new_col = self[name] # Check base DataInfo attributes that any column will have for attr in DataInfo.attr_names: if getattr(old_col.info, attr) != getattr(new_col.info, attr): changed_attrs.append(attr) if changed_attrs: msg = ( f"replaced column '{name}' and column attributes " f"{changed_attrs} changed." ) warnings.warn(msg, TableReplaceWarning, stacklevel=3)
[docs] def replace_column(self, name, col, copy=True): """ Replace column ``name`` with the new ``col`` object. The behavior of ``copy`` for Column objects is: - copy=True: new class instance with a copy of data and deep copy of meta - copy=False: new class instance with same data and a key-only copy of meta For mixin columns: - copy=True: new class instance with copy of data and deep copy of meta - copy=False: original instance (no copy at all) Parameters ---------- name : str Name of column to replace col : `~astropy.table.Column` or `~numpy.ndarray` or sequence New column object to replace the existing column. copy : bool Make copy of the input ``col``, default=True See Also -------- add_columns, astropy.table.hstack, update Examples -------- Replace column 'a' with a float version of itself:: >>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3]], names=('a', 'b')) >>> float_a = t['a'].astype(float) >>> t.replace_column('a', float_a) """ if name not in self.colnames: raise ValueError(f"column name {name} is not in the table") if self[name].info.indices: raise ValueError("cannot replace a table index column") col = self._convert_data_to_col(col, name=name, copy=copy) self._set_col_parent_table_and_mask(col) # Ensure that new column is the right length, unless it is the only column # in which case re-sizing is allowed. if len(self.columns) > 1 and len(col) != len(self[name]): raise ValueError("length of new column must match table length") self.columns.__setitem__(name, col, validated=True)
[docs] def remove_row(self, index): """ Remove a row from the table. Parameters ---------- index : int Index of row to remove Examples -------- Create a table with three columns 'a', 'b' and 'c':: >>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']], ... names=('a', 'b', 'c')) >>> print(t) a b c --- --- --- 1 0.1 x 2 0.2 y 3 0.3 z Remove row 1 from the table:: >>> t.remove_row(1) >>> print(t) a b c --- --- --- 1 0.1 x 3 0.3 z To remove several rows at the same time use remove_rows. """ # check the index against the types that work with np.delete if not isinstance(index, (int, np.integer)): raise TypeError("Row index must be an integer") self.remove_rows(index)
[docs] def remove_rows(self, row_specifier): """ Remove rows from the table. Parameters ---------- row_specifier : slice or int or array of int Specification for rows to remove Examples -------- Create a table with three columns 'a', 'b' and 'c':: >>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']], ... names=('a', 'b', 'c')) >>> print(t) a b c --- --- --- 1 0.1 x 2 0.2 y 3 0.3 z Remove rows 0 and 2 from the table:: >>> t.remove_rows([0, 2]) >>> print(t) a b c --- --- --- 2 0.2 y Note that there are no warnings if the slice operator extends outside the data:: >>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']], ... names=('a', 'b', 'c')) >>> t.remove_rows(slice(10, 20, 1)) >>> print(t) a b c --- --- --- 1 0.1 x 2 0.2 y 3 0.3 z """ # Update indices for index in self.indices: index.remove_rows(row_specifier) keep_mask = np.ones(len(self), dtype=bool) keep_mask[row_specifier] = False columns = self.TableColumns() for name, col in self.columns.items(): newcol = col[keep_mask] newcol.info.parent_table = self columns[name] = newcol self._replace_cols(columns) # Revert groups to default (ungrouped) state if hasattr(self, "_groups"): del self._groups
[docs] def iterrows(self, *names): """ Iterate over rows of table returning a tuple of values for each row. This method is especially useful when only a subset of columns are needed. The ``iterrows`` method can be substantially faster than using the standard Table row iteration (e.g. ``for row in tbl:``), since that returns a new ``~astropy.table.Row`` object for each row and accessing a column in that row (e.g. ``row['col0']``) is slower than tuple access. Parameters ---------- names : list List of column names (default to all columns if no names provided) Returns ------- rows : iterable Iterator returns tuples of row values Examples -------- Create a table with three columns 'a', 'b' and 'c':: >>> t = Table({'a': [1, 2, 3], ... 'b': [1.0, 2.5, 3.0], ... 'c': ['x', 'y', 'z']}) To iterate row-wise using column names:: >>> for a, c in t.iterrows('a', 'c'): ... print(a, c) 1 x 2 y 3 z """ if len(names) == 0: names = self.colnames else: for name in names: if name not in self.colnames: raise ValueError(f"{name} is not a valid column name") cols = (self[name] for name in names) out = zip(*cols) return out
def _set_of_names_in_colnames(self, names): """Return ``names`` as a set if valid, or raise a `KeyError`. ``names`` is valid if all elements in it are in ``self.colnames``. If ``names`` is a string then it is interpreted as a single column name. """ names = {names} if isinstance(names, str) else set(names) invalid_names = names.difference(self.colnames) if len(invalid_names) == 1: raise KeyError(f'column "{invalid_names.pop()}" does not exist') elif len(invalid_names) > 1: raise KeyError(f"columns {invalid_names} do not exist") return names
[docs] def remove_column(self, name): """ Remove a column from the table. This can also be done with:: del table[name] Parameters ---------- name : str Name of column to remove Examples -------- Create a table with three columns 'a', 'b' and 'c':: >>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']], ... names=('a', 'b', 'c')) >>> print(t) a b c --- --- --- 1 0.1 x 2 0.2 y 3 0.3 z Remove column 'b' from the table:: >>> t.remove_column('b') >>> print(t) a c --- --- 1 x 2 y 3 z To remove several columns at the same time use remove_columns. """ self.remove_columns([name])
[docs] def remove_columns(self, names): """ Remove several columns from the table. Parameters ---------- names : str or iterable of str Names of the columns to remove Examples -------- Create a table with three columns 'a', 'b' and 'c':: >>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']], ... names=('a', 'b', 'c')) >>> print(t) a b c --- --- --- 1 0.1 x 2 0.2 y 3 0.3 z Remove columns 'b' and 'c' from the table:: >>> t.remove_columns(['b', 'c']) >>> print(t) a --- 1 2 3 Specifying only a single column also works. Remove column 'b' from the table:: >>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']], ... names=('a', 'b', 'c')) >>> t.remove_columns('b') >>> print(t) a c --- --- 1 x 2 y 3 z This gives the same as using remove_column. """ for name in self._set_of_names_in_colnames(names): del self.columns[name]
def _convert_string_dtype(self, in_kind, out_kind, encode_decode_func): """ Convert string-like columns to/from bytestring and unicode (internal only). Parameters ---------- in_kind : str Input dtype.kind out_kind : str Output dtype.kind """ for col in self.itercols(): if col.dtype.kind == in_kind: try: # This requires ASCII and is faster by a factor of up to ~8, so # try that first. newcol = col.__class__(col, dtype=out_kind) except (UnicodeEncodeError, UnicodeDecodeError): newcol = col.__class__(encode_decode_func(col, "utf-8")) # Quasi-manually copy info attributes. Unfortunately # DataInfo.__set__ does not do the right thing in this case # so newcol.info = col.info does not get the old info attributes. for attr in ( col.info.attr_names - col.info._attrs_no_copy - {"dtype"} ): value = deepcopy(getattr(col.info, attr)) setattr(newcol.info, attr, value) self[col.name] = newcol
[docs] def convert_bytestring_to_unicode(self): """ Convert bytestring columns (dtype.kind='S') to unicode (dtype.kind='U') using UTF-8 encoding. Internally this changes string columns to represent each character in the string with a 4-byte UCS-4 equivalent, so it is inefficient for memory but allows scripts to manipulate string arrays with natural syntax. """ self._convert_string_dtype("S", "U", np.char.decode)
[docs] def convert_unicode_to_bytestring(self): """ Convert unicode columns (dtype.kind='U') to bytestring (dtype.kind='S') using UTF-8 encoding. When exporting a unicode string array to a file, it may be desirable to encode unicode columns as bytestrings. """ self._convert_string_dtype("U", "S", np.char.encode)
[docs] def keep_columns(self, names): """ Keep only the columns specified (remove the others). Parameters ---------- names : str or iterable of str The columns to keep. All other columns will be removed. Examples -------- Create a table with three columns 'a', 'b' and 'c':: >>> t = Table([[1, 2, 3],[0.1, 0.2, 0.3],['x', 'y', 'z']], ... names=('a', 'b', 'c')) >>> print(t) a b c --- --- --- 1 0.1 x 2 0.2 y 3 0.3 z Keep only column 'a' of the table:: >>> t.keep_columns('a') >>> print(t) a --- 1 2 3 Keep columns 'a' and 'c' of the table:: >>> t = Table([[1, 2, 3],[0.1, 0.2, 0.3],['x', 'y', 'z']], ... names=('a', 'b', 'c')) >>> t.keep_columns(['a', 'c']) >>> print(t) a c --- --- 1 x 2 y 3 z """ names = self._set_of_names_in_colnames(names) for colname in self.colnames: if colname not in names: del self.columns[colname]
[docs] def rename_column(self, name, new_name): """ Rename a column. This can also be done directly by setting the ``name`` attribute of the ``info`` property of the column:: table[name].info.name = new_name Parameters ---------- name : str The current name of the column. new_name : str The new name for the column Examples -------- Create a table with three columns 'a', 'b' and 'c':: >>> t = Table([[1,2],[3,4],[5,6]], names=('a','b','c')) >>> print(t) a b c --- --- --- 1 3 5 2 4 6 Renaming column 'a' to 'aa':: >>> t.rename_column('a' , 'aa') >>> print(t) aa b c --- --- --- 1 3 5 2 4 6 """ if name not in self.keys(): raise KeyError(f"Column {name} does not exist") self.columns[name].info.name = new_name
[docs] def rename_columns(self, names, new_names): """ Rename multiple columns. Parameters ---------- names : list, tuple A list or tuple of existing column names. new_names : list, tuple A list or tuple of new column names. Examples -------- Create a table with three columns 'a', 'b', 'c':: >>> t = Table([[1,2],[3,4],[5,6]], names=('a','b','c')) >>> print(t) a b c --- --- --- 1 3 5 2 4 6 Renaming columns 'a' to 'aa' and 'b' to 'bb':: >>> names = ('a','b') >>> new_names = ('aa','bb') >>> t.rename_columns(names, new_names) >>> print(t) aa bb c --- --- --- 1 3 5 2 4 6 """ if not self._is_list_or_tuple_of_str(names): raise TypeError("input 'names' must be a tuple or a list of column names") if not self._is_list_or_tuple_of_str(new_names): raise TypeError( "input 'new_names' must be a tuple or a list of column names" ) if len(names) != len(new_names): raise ValueError( "input 'names' and 'new_names' list arguments must be the same length" ) for name, new_name in zip(names, new_names): self.rename_column(name, new_name)
def _set_row(self, idx, colnames, vals): try: assert len(vals) == len(colnames) except Exception: raise ValueError( "right hand side must be a sequence of values with " "the same length as the number of selected columns" ) # Keep track of original values before setting each column so that # setting row can be transactional. orig_vals = [] cols = self.columns try: for name, val in zip(colnames, vals): orig_vals.append(cols[name][idx]) cols[name][idx] = val except Exception: # If anything went wrong first revert the row update then raise for name, val in zip(colnames, orig_vals[:-1]): cols[name][idx] = val raise
[docs] def add_row(self, vals=None, mask=None): """Add a new row to the end of the table. The ``vals`` argument can be: sequence (e.g. tuple or list) Column values in the same order as table columns. mapping (e.g. dict) Keys corresponding to column names. Missing values will be filled with np.zeros for the column dtype. `None` All values filled with np.zeros for the column dtype. This method requires that the Table object "owns" the underlying array data. In particular one cannot add a row to a Table that was initialized with copy=False from an existing array. The ``mask`` attribute should give (if desired) the mask for the values. The type of the mask should match that of the values, i.e. if ``vals`` is an iterable, then ``mask`` should also be an iterable with the same length, and if ``vals`` is a mapping, then ``mask`` should be a dictionary. Parameters ---------- vals : tuple, list, dict or None Use the specified values in the new row mask : tuple, list, dict or None Use the specified mask values in the new row Examples -------- Create a table with three columns 'a', 'b' and 'c':: >>> t = Table([[1,2],[4,5],[7,8]], names=('a','b','c')) >>> print(t) a b c --- --- --- 1 4 7 2 5 8 Adding a new row with entries '3' in 'a', '6' in 'b' and '9' in 'c':: >>> t.add_row([3,6,9]) >>> print(t) a b c --- --- --- 1 4 7 2 5 8 3 6 9 """ self.insert_row(len(self), vals, mask)
[docs] def insert_row(self, index, vals=None, mask=None): """Add a new row before the given ``index`` position in the table. The ``vals`` argument can be: sequence (e.g. tuple or list) Column values in the same order as table columns. mapping (e.g. dict) Keys corresponding to column names. Missing values will be filled with np.zeros for the column dtype. `None` All values filled with np.zeros for the column dtype. The ``mask`` attribute should give (if desired) the mask for the values. The type of the mask should match that of the values, i.e. if ``vals`` is an iterable, then ``mask`` should also be an iterable with the same length, and if ``vals`` is a mapping, then ``mask`` should be a dictionary. Parameters ---------- vals : tuple, list, dict or None Use the specified values in the new row mask : tuple, list, dict or None Use the specified mask values in the new row """ colnames = self.colnames N = len(self) if index < -N or index > N: raise IndexError( f"Index {index} is out of bounds for table with length {N}" ) if index < 0: index += N if isinstance(vals, Mapping) or vals is None: # From the vals and/or mask mappings create the corresponding lists # that have entries for each table column. if mask is not None and not isinstance(mask, Mapping): raise TypeError("Mismatch between type of vals and mask") # Now check that the mask is specified for the same keys as the # values, otherwise things get really confusing. if mask is not None and set(vals.keys()) != set(mask.keys()): raise ValueError("keys in mask should match keys in vals") if vals and any(name not in colnames for name in vals): raise ValueError("Keys in vals must all be valid column names") vals_list = [] mask_list = [] for name in colnames: if vals and name in vals: vals_list.append(vals[name]) mask_list.append(False if mask is None else mask[name]) else: col = self[name] if hasattr(col, "dtype"): # Make a placeholder zero element of the right type which is masked. # This assumes the appropriate insert() method will broadcast a # numpy scalar to the right shape. vals_list.append(np.zeros(shape=(), dtype=col.dtype)) # For masked table any unsupplied values are masked by default. mask_list.append(self.masked and vals is not None) else: raise ValueError(f"Value must be supplied for column '{name}'") vals = vals_list mask = mask_list if isiterable(vals): if mask is not None and (not isiterable(mask) or isinstance(mask, Mapping)): raise TypeError("Mismatch between type of vals and mask") if len(self.columns) != len(vals): raise ValueError("Mismatch between number of vals and columns") if mask is not None: if len(self.columns) != len(mask): raise ValueError("Mismatch between number of masks and columns") else: mask = [False] * len(self.columns) else: raise TypeError("Vals must be an iterable or mapping or None") if N == 0 and any( isinstance(column, BaseColumn) and isinstance(v, Quantity) for column, v in zip(self.columns.values(), vals) ): msg = "Units from inserted quantities will be ignored." if isinstance(self, QTable): suggested_units = [] for column, v in zip(self.columns.values(), vals): u = column.unit or getattr(v, "unit", None) suggested_units.append(str(u) if u is not None else None) del u msg += ( "\nIf you were hoping to fill a QTable row by row, " "also initialize the units before starting, for instance\n" f"QTable(names={self.colnames}, units={suggested_units})" ) del suggested_units warnings.warn(msg, category=UserWarning, stacklevel=2) del msg # Insert val at index for each column columns = self.TableColumns() for name, col, val, mask_ in zip(colnames, self.columns.values(), vals, mask): try: # If new val is masked and the existing column does not support masking # then upgrade the column to a mask-enabled type: either the table-level # default ColumnClass or else MaskedColumn. if ( mask_ and isinstance(col, Column) and not isinstance(col, MaskedColumn) ): col_cls = ( self.ColumnClass if issubclass(self.ColumnClass, self.MaskedColumn) else self.MaskedColumn ) col = col_cls(col, copy=False) newcol = col.insert(index, val, axis=0) if len(newcol) != N + 1: raise ValueError( f"Incorrect length for column {name} after inserting {val}" f" (expected {len(newcol)}, got {N + 1})" ) newcol.info.parent_table = self # Set mask if needed and possible if mask_: if hasattr(newcol, "mask"): newcol[index] = np.ma.masked else: raise TypeError( f"mask was supplied for column '{col.info.name}' " "but it does not support masked values" ) columns[name] = newcol except Exception as err: raise ValueError( f"Unable to insert row because of exception in column '{name}':\n{err}" ) from err for table_index in self.indices: table_index.insert_row(index, vals, self.columns.values()) self._replace_cols(columns) # Revert groups to default (ungrouped) state if hasattr(self, "_groups"): del self._groups
def _replace_cols(self, columns): for col, new_col in zip(self.columns.values(), columns.values()): new_col.info.indices = [] for index in col.info.indices: index.columns[index.col_position(col.info.name)] = new_col new_col.info.indices.append(index) self.columns = columns
[docs] def setdefault(self, name, default): """Ensure a column named ``name`` exists. If ``name`` is already present then ``default`` is ignored. Otherwise ``default`` can be any data object which is acceptable as a `~astropy.table.Table` column object or can be converted. This includes mixin columns and scalar or length=1 objects which get broadcast to match the table length. Parameters ---------- name : str Name of the column. default : object Data object for the new column. Returns ------- `~astropy.table.Column`, `~astropy.table.MaskedColumn` or mixin-column type The column named ``name`` if it is present already, or the validated ``default`` converted to a column otherwise. Raises ------ TypeError If the table is empty and ``default`` is a scalar object. Examples -------- Start with a simple table:: >>> t0 = Table({"a": ["Ham", "Spam"]}) >>> t0 <Table length=2> a str4 ---- Ham Spam Trying to add a column that already exists does not modify it:: >>> t0.setdefault("a", ["Breakfast"]) <Column name='a' dtype='str4' length=2> Ham Spam >>> t0 <Table length=2> a str4 ---- Ham Spam But if the column does not exist it will be created with the default value:: >>> t0.setdefault("approved", False) <Column name='approved' dtype='bool' length=2> False False >>> t0 <Table length=2> a approved str4 bool ---- -------- Ham False Spam False """ if name not in self.columns: self[name] = default return self[name]
[docs] def update(self, other, copy=True): """ Perform a dictionary-style update and merge metadata. The argument ``other`` must be a |Table|, or something that can be used to initialize a table. Columns from (possibly converted) ``other`` are added to this table. In case of matching column names the column from this table is replaced with the one from ``other``. If ``other`` is a |Table| instance then ``|=`` is available as alternate syntax for in-place update and ``|`` can be used merge data to a new table. Parameters ---------- other : table-like Data to update this table with. copy : bool Whether the updated columns should be copies of or references to the originals. See Also -------- add_columns, astropy.table.hstack, replace_column Examples -------- Update a table with another table:: >>> t1 = Table({'a': ['foo', 'bar'], 'b': [0., 0.]}, meta={'i': 0}) >>> t2 = Table({'b': [1., 2.], 'c': [7., 11.]}, meta={'n': 2}) >>> t1.update(t2) >>> t1 <Table length=2> a b c str3 float64 float64 ---- ------- ------- foo 1.0 7.0 bar 2.0 11.0 >>> t1.meta {'i': 0, 'n': 2} Update a table with a dictionary:: >>> t = Table({'a': ['foo', 'bar'], 'b': [0., 0.]}) >>> t.update({'b': [1., 2.]}) >>> t <Table length=2> a b str3 float64 ---- ------- foo 1.0 bar 2.0 """ from .operations import _merge_table_meta if not isinstance(other, Table): other = self.__class__(other, copy=copy) common_cols = set(self.colnames).intersection(other.colnames) for name, col in other.items(): if name in common_cols: self.replace_column(name, col, copy=copy) else: self.add_column(col, name=name, copy=copy) _merge_table_meta(self, [self, other], metadata_conflicts="silent")
[docs] def argsort(self, keys=None, kind=None, reverse=False): """ Return the indices which would sort the table according to one or more key columns. This simply calls the `numpy.argsort` function on the table with the ``order`` parameter set to ``keys``. Parameters ---------- keys : str or list of str The column name(s) to order the table by kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, optional Sorting algorithm used by ``numpy.argsort``. reverse : bool Sort in reverse order (default=False) Returns ------- index_array : ndarray, int Array of indices that sorts the table by the specified key column(s). """ if isinstance(keys, str): keys = [keys] # use index sorted order if possible if keys is not None: index = get_index(self, names=keys) if index is not None: idx = np.asarray(index.sorted_data()) return idx[::-1] if reverse else idx kwargs = {} if keys: # For multiple keys return a structured array which gets sorted, # while for a single key return a single ndarray. Sorting a # one-column structured array is slower than ndarray (e.g. a # factor of ~6 for a 10 million long random array), and much slower # for in principle sortable columns like Time, which get stored as # object arrays. if len(keys) > 1: kwargs["order"] = keys data = self.as_array(names=keys) else: data = self[keys[0]] else: # No keys provided so sort on all columns. data = self.as_array() if kind: kwargs["kind"] = kind # np.argsort will look for a possible .argsort method (e.g., for Time), # and if that fails cast to an array and try sorting that way. idx = np.argsort(data, **kwargs) return idx[::-1] if reverse else idx
[docs] def sort(self, keys=None, *, kind=None, reverse=False): """ Sort the table according to one or more keys. This operates on the existing table and does not return a new table. Parameters ---------- keys : str or list of str The key(s) to order the table by. If None, use the primary index of the Table. kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, optional Sorting algorithm used by ``numpy.argsort``. reverse : bool Sort in reverse order (default=False) Examples -------- Create a table with 3 columns:: >>> t = Table([['Max', 'Jo', 'John'], ['Miller', 'Miller', 'Jackson'], ... [12, 15, 18]], names=('firstname', 'name', 'tel')) >>> print(t) firstname name tel --------- ------- --- Max Miller 12 Jo Miller 15 John Jackson 18 Sorting according to standard sorting rules, first 'name' then 'firstname':: >>> t.sort(['name', 'firstname']) >>> print(t) firstname name tel --------- ------- --- John Jackson 18 Jo Miller 15 Max Miller 12 Sorting according to standard sorting rules, first 'firstname' then 'tel', in reverse order:: >>> t.sort(['firstname', 'tel'], reverse=True) >>> print(t) firstname name tel --------- ------- --- Max Miller 12 John Jackson 18 Jo Miller 15 """ if keys is None: if not self.indices: raise ValueError("Table sort requires input keys or a table index") keys = [x.info.name for x in self.indices[0].columns] if isinstance(keys, str): keys = [keys] indexes = self.argsort(keys, kind=kind, reverse=reverse) with self.index_mode("freeze"): for col in self.columns.values(): # Make a new sorted column. This requires that take() also copies # relevant info attributes for mixin columns. new_col = col.take(indexes, axis=0) # First statement in try: will succeed if the column supports an in-place # update, and matches the legacy behavior of astropy Table. However, # some mixin classes may not support this, so in that case just drop # in the entire new column. See #9553 and #9536 for discussion. try: col[:] = new_col except Exception: # In-place update failed for some reason, exception class not # predictable for arbitrary mixin. self[col.info.name] = new_col
[docs] def reverse(self): """ Reverse the row order of table rows. The table is reversed in place and there are no function arguments. Examples -------- Create a table with three columns:: >>> t = Table([['Max', 'Jo', 'John'], ['Miller','Miller','Jackson'], ... [12,15,18]], names=('firstname','name','tel')) >>> print(t) firstname name tel --------- ------- --- Max Miller 12 Jo Miller 15 John Jackson 18 Reversing order:: >>> t.reverse() >>> print(t) firstname name tel --------- ------- --- John Jackson 18 Jo Miller 15 Max Miller 12 """ for col in self.columns.values(): # First statement in try: will succeed if the column supports an in-place # update, and matches the legacy behavior of astropy Table. However, # some mixin classes may not support this, so in that case just drop # in the entire new column. See #9836, #9553, and #9536 for discussion. new_col = col[::-1] try: col[:] = new_col except Exception: # In-place update failed for some reason, exception class not # predictable for arbitrary mixin. self[col.info.name] = new_col for index in self.indices: index.reverse()
[docs] def round(self, decimals=0): """ Round numeric columns in-place to the specified number of decimals. Non-numeric columns will be ignored. Examples -------- Create three columns with different types: >>> t = Table([[1, 4, 5], [-25.55, 12.123, 85], ... ['a', 'b', 'c']], names=('a', 'b', 'c')) >>> print(t) a b c --- ------ --- 1 -25.55 a 4 12.123 b 5 85.0 c Round them all to 0: >>> t.round(0) >>> print(t) a b c --- ----- --- 1 -26.0 a 4 12.0 b 5 85.0 c Round column 'a' to -1 decimal: >>> t.round({'a':-1}) >>> print(t) a b c --- ----- --- 0 -26.0 a 0 12.0 b 0 85.0 c Parameters ---------- decimals: int, dict Number of decimals to round the columns to. If a dict is given, the columns will be rounded to the number specified as the value. If a certain column is not in the dict given, it will remain the same. """ if isinstance(decimals, Mapping): decimal_values = decimals.values() column_names = decimals.keys() elif isinstance(decimals, int): decimal_values = itertools.repeat(decimals) column_names = self.colnames else: raise ValueError("'decimals' argument must be an int or a dict") for colname, decimal in zip(column_names, decimal_values): col = self.columns[colname] if np.issubdtype(col.info.dtype, np.number): try: np.around(col, decimals=decimal, out=col) except TypeError: # Bug in numpy see https://github.com/numpy/numpy/issues/15438 col[()] = np.around(col, decimals=decimal)
[docs] def copy(self, copy_data=True): """ Return a copy of the table. Parameters ---------- copy_data : bool If `True` (the default), copy the underlying data array and make a deep copy of the ``meta`` attribute. Otherwise, use the same data array and make a shallow (key-only) copy of ``meta``. """ out = self.__class__(self, copy=copy_data) # If the current table is grouped then do the same in the copy if hasattr(self, "_groups"): out._groups = groups.TableGroups( out, indices=self._groups._indices, keys=self._groups._keys ) return out
def __deepcopy__(self, memo=None): out = self.copy(False) for name in out.colnames: out.columns.__setitem__(name, deepcopy(self[name]), validated=True) out.meta = deepcopy(self.meta) return out def __copy__(self): return self.copy(False) def __eq__(self, other): return self._rows_equal(other) def __ne__(self, other): eq = self.__eq__(other) if isinstance(eq, bool): # bitwise operators on bool values not reliable (e.g. `bool(~True) == True`) # and are deprecated in Python 3.12 # see https://github.com/python/cpython/pull/103487 return not eq else: return ~eq def _rows_equal(self, other): """ Row-wise comparison of table with any other object. This is actual implementation for __eq__. Returns a 1-D boolean numpy array showing result of row-wise comparison, or a bool (False) in cases where comparison isn't possible (uncomparable dtypes or unbroadcastable shapes). Intended to follow legacy numpy's elementwise comparison rules. This is the same as the ``==`` comparison for tables. Parameters ---------- other : Table or DataFrame or ndarray An object to compare with table Examples -------- Comparing one Table with other:: >>> t1 = Table([[1,2],[4,5],[7,8]], names=('a','b','c')) >>> t2 = Table([[1,2],[4,5],[7,8]], names=('a','b','c')) >>> t1._rows_equal(t2) array([ True, True]) """ if isinstance(other, Table): other = other.as_array() self_is_masked = self.has_masked_columns other_is_masked = isinstance(other, np.ma.MaskedArray) allowed_numpy_exceptions = ( TypeError, ValueError if not NUMPY_LT_1_25 else DeprecationWarning, ) # One table is masked and the other is not if self_is_masked ^ other_is_masked: # remap variables to a and b where a is masked and b isn't a, b = ( (self.as_array(), other) if self_is_masked else (other, self.as_array()) ) # If mask is True, then by definition the row doesn't match # because the other array is not masked. false_mask = np.zeros(1, dtype=[(n, bool) for n in a.dtype.names]) try: result = (a.data == b) & (a.mask == false_mask) except allowed_numpy_exceptions: # numpy may complain that structured array are not comparable (TypeError) # or that operands are not brodcastable (ValueError) # see https://github.com/astropy/astropy/issues/13421 result = False else: try: result = self.as_array() == other except allowed_numpy_exceptions: result = False return result
[docs] def values_equal(self, other): """ Element-wise comparison of table with another table, list, or scalar. Returns a ``Table`` with the same columns containing boolean values showing result of comparison. Parameters ---------- other : table-like object or list or scalar Object to compare with table Examples -------- Compare one Table with other:: >>> t1 = Table([[1, 2], [4, 5], [-7, 8]], names=('a', 'b', 'c')) >>> t2 = Table([[1, 2], [-4, 5], [7, 8]], names=('a', 'b', 'c')) >>> t1.values_equal(t2) <Table length=2> a b c bool bool bool ---- ----- ----- True False False True True True """ if isinstance(other, Table): names = other.colnames else: try: other = Table(other, copy=False) names = other.colnames except Exception: # Broadcast other into a dict, so e.g. other = 2 will turn into # other = {'a': 2, 'b': 2} and then equality does a # column-by-column broadcasting. names = self.colnames other = {name: other for name in names} # Require column names match but do not require same column order if set(self.colnames) != set(names): raise ValueError("cannot compare tables with different column names") eqs = [] for name in names: try: np.broadcast(self[name], other[name]) # Check if broadcast-able # Catch the numpy FutureWarning related to equality checking, # "elementwise comparison failed; returning scalar instead, but # in the future will perform elementwise comparison". Turn this # into an exception since the scalar answer is not what we want. with warnings.catch_warnings(record=True) as warns: warnings.simplefilter("always") eq = self[name] == other[name] if ( warns and issubclass(warns[-1].category, FutureWarning) and "elementwise comparison failed" in str(warns[-1].message) ): raise FutureWarning(warns[-1].message) except Exception as err: raise ValueError(f"unable to compare column {name}") from err # Be strict about the result from the comparison. E.g. SkyCoord __eq__ is just # broken and completely ignores that it should return an array. if not ( isinstance(eq, np.ndarray) and eq.dtype is np.dtype("bool") and len(eq) == len(self) ): raise TypeError( f"comparison for column {name} returned {eq} " "instead of the expected boolean ndarray" ) eqs.append(eq) out = Table(eqs, names=names) return out
@property def groups(self): if not hasattr(self, "_groups"): self._groups = groups.TableGroups(self) return self._groups
[docs] def group_by(self, keys): """ Group this table by the specified ``keys``. This effectively splits the table into groups which correspond to unique values of the ``keys`` grouping object. The output is a new `~astropy.table.TableGroups` which contains a copy of this table but sorted by row according to ``keys``. The ``keys`` input to `group_by` can be specified in different ways: - String or list of strings corresponding to table column name(s) - Numpy array (homogeneous or structured) with same length as this table - `~astropy.table.Table` with same length as this table Parameters ---------- keys : str, list of str, numpy array, or `~astropy.table.Table` Key grouping object Returns ------- out : `~astropy.table.Table` New table with groups set """ return groups.table_group_by(self, keys)
[docs] def to_pandas(self, index=None, use_nullable_int=True): """ Return a :class:`pandas.DataFrame` instance. The index of the created DataFrame is controlled by the ``index`` argument. For ``index=True`` or the default ``None``, an index will be specified for the DataFrame if there is a primary key index on the Table *and* if it corresponds to a single column. If ``index=False`` then no DataFrame index will be specified. If ``index`` is the name of a column in the table then that will be the DataFrame index. In addition to vanilla columns or masked columns, this supports Table mixin columns like Quantity, Time, or SkyCoord. In many cases these objects have no analog in pandas and will be converted to a "encoded" representation using only Column or MaskedColumn. The exception is Time or TimeDelta columns, which will be converted to the corresponding representation in pandas using ``np.datetime64`` or ``np.timedelta64``. See the example below. Parameters ---------- index : None, bool, str Specify DataFrame index mode use_nullable_int : bool, default=True Convert integer MaskedColumn to pandas nullable integer type. If ``use_nullable_int=False`` or the pandas version does not support nullable integer types (version < 0.24), then the column is converted to float with NaN for missing elements and a warning is issued. Returns ------- dataframe : :class:`pandas.DataFrame` A pandas :class:`pandas.DataFrame` instance Raises ------ ImportError If pandas is not installed ValueError If the Table has multi-dimensional columns Examples -------- Here we convert a table with a few mixins to a :class:`pandas.DataFrame` instance. >>> import pandas as pd >>> from astropy.table import QTable >>> import astropy.units as u >>> from astropy.time import Time, TimeDelta >>> from astropy.coordinates import SkyCoord >>> q = [1, 2] * u.m >>> tm = Time([1998, 2002], format='jyear') >>> sc = SkyCoord([5, 6], [7, 8], unit='deg') >>> dt = TimeDelta([3, 200] * u.s) >>> t = QTable([q, tm, sc, dt], names=['q', 'tm', 'sc', 'dt']) >>> df = t.to_pandas(index='tm') >>> with pd.option_context('display.max_columns', 20): ... print(df) q sc.ra sc.dec dt tm 1998-01-01 1.0 5.0 7.0 0 days 00:00:03 2002-01-01 2.0 6.0 8.0 0 days 00:03:20 """ from pandas import DataFrame, Series if index is not False: if index in (None, True): # Default is to use the table primary key if available and a single column if self.primary_key and len(self.primary_key) == 1: index = self.primary_key[0] else: index = False else: if index not in self.colnames: raise ValueError( "index must be None, False, True or a table column name" ) def _encode_mixins(tbl): """Encode a Table ``tbl`` that may have mixin columns to a Table with only astropy Columns + appropriate meta-data to allow subsequent decoding. """ from astropy.time import TimeBase, TimeDelta from . import serialize # Convert any Time or TimeDelta columns and pay attention to masking time_cols = [col for col in tbl.itercols() if isinstance(col, TimeBase)] if time_cols: # Make a light copy of table and clear any indices new_cols = [] for col in tbl.itercols(): new_col = ( col_copy(col, copy_indices=False) if col.info.indices else col ) new_cols.append(new_col) tbl = tbl.__class__(new_cols, copy=False) # Certain subclasses (e.g. TimeSeries) may generate new indices on # table creation, so make sure there are no indices on the table. for col in tbl.itercols(): col.info.indices.clear() for col in time_cols: if isinstance(col, TimeDelta): # Convert to nanoseconds (matches astropy datetime64 support) new_col = (col.sec * 1e9).astype("timedelta64[ns]") nat = np.timedelta64("NaT") else: new_col = col.datetime64.copy() nat = np.datetime64("NaT") if col.masked: new_col[col.mask] = nat tbl[col.info.name] = new_col # Convert the table to one with no mixins, only Column objects. encode_tbl = serialize.represent_mixins_as_columns(tbl) return encode_tbl tbl = _encode_mixins(self) badcols = [name for name, col in self.columns.items() if len(col.shape) > 1] if badcols: # fmt: off raise ValueError( f'Cannot convert a table with multidimensional columns to a ' f'pandas DataFrame. Offending columns are: {badcols}\n' f'One can filter out such columns using:\n' f'names = [name for name in tbl.colnames if len(tbl[name].shape) <= 1]\n' f'tbl[names].to_pandas(...)' ) # fmt: on out = OrderedDict() for name, column in tbl.columns.items(): if getattr(column.dtype, "isnative", True): out[name] = column else: out[name] = column.data.byteswap().view(column.dtype.newbyteorder("=")) if isinstance(column, MaskedColumn) and np.any(column.mask): if column.dtype.kind in ["i", "u"]: pd_dtype = column.dtype.name if use_nullable_int: # Convert int64 to Int64, uint32 to UInt32, etc for nullable types pd_dtype = pd_dtype.replace("i", "I").replace("u", "U") out[name] = Series(out[name], dtype=pd_dtype) # If pandas is older than 0.24 the type may have turned to float if column.dtype.kind != out[name].dtype.kind: warnings.warn( f"converted column '{name}' from {column.dtype} to" f" {out[name].dtype}", TableReplaceWarning, stacklevel=3, ) elif column.dtype.kind not in ["f", "c"]: out[name] = column.astype(object).filled(np.nan) kwargs = {} if index: idx = out.pop(index) kwargs["index"] = idx # We add the table index to Series inputs (MaskedColumn with int values) to override # its default RangeIndex, see #11432 for v in out.values(): if isinstance(v, Series): v.index = idx df = DataFrame(out, **kwargs) if index: # Explicitly set the pandas DataFrame index to the original table # index name. df.index.name = idx.info.name return df
[docs] @classmethod def from_pandas(cls, dataframe, index=False, units=None): """ Create a `~astropy.table.Table` from a :class:`pandas.DataFrame` instance. In addition to converting generic numeric or string columns, this supports conversion of pandas Date and Time delta columns to `~astropy.time.Time` and `~astropy.time.TimeDelta` columns, respectively. Parameters ---------- dataframe : :class:`pandas.DataFrame` A pandas :class:`pandas.DataFrame` instance index : bool Include the index column in the returned table (default=False) units: dict A dict mapping column names to a `~astropy.units.Unit`. The columns will have the specified unit in the Table. Returns ------- table : `~astropy.table.Table` A `~astropy.table.Table` (or subclass) instance Raises ------ ImportError If pandas is not installed Examples -------- Here we convert a :class:`pandas.DataFrame` instance to a `~astropy.table.QTable`. >>> import numpy as np >>> import pandas as pd >>> from astropy.table import QTable >>> time = pd.Series(['1998-01-01', '2002-01-01'], dtype='datetime64[ns]') >>> dt = pd.Series(np.array([1, 300], dtype='timedelta64[s]')) >>> df = pd.DataFrame({'time': time}) >>> df['dt'] = dt >>> df['x'] = [3., 4.] >>> with pd.option_context('display.max_columns', 20): ... print(df) time dt x 0 1998-01-01 0 days 00:00:01 3.0 1 2002-01-01 0 days 00:05:00 4.0 >>> QTable.from_pandas(df) <QTable length=2> time dt x Time TimeDelta float64 ----------------------- --------- ------- 1998-01-01T00:00:00.000 1.0 3.0 2002-01-01T00:00:00.000 300.0 4.0 """ out = OrderedDict() names = list(dataframe.columns) columns = [dataframe[name] for name in names] datas = [np.array(column) for column in columns] masks = [np.array(column.isnull()) for column in columns] if index: index_name = dataframe.index.name or "index" while index_name in names: index_name = "_" + index_name + "_" names.insert(0, index_name) columns.insert(0, dataframe.index) datas.insert(0, np.array(dataframe.index)) masks.insert(0, np.zeros(len(dataframe), dtype=bool)) if units is None: units = [None] * len(names) else: if not isinstance(units, Mapping): raise TypeError('Expected a Mapping "column-name" -> "unit"') not_found = set(units.keys()) - set(names) if not_found: warnings.warn(f"`units` contains additional columns: {not_found}") units = [units.get(name) for name in names] for name, column, data, mask, unit in zip(names, columns, datas, masks, units): if column.dtype.kind in ["u", "i"] and np.any(mask): # Special-case support for pandas nullable int np_dtype = str(column.dtype).lower() data = np.zeros(shape=column.shape, dtype=np_dtype) data[~mask] = column[~mask] out[name] = MaskedColumn( data=data, name=name, mask=mask, unit=unit, copy=False ) continue if data.dtype.kind == "O": # If all elements of an object array are string-like or np.nan # then coerce back to a native numpy str/unicode array. string_types = (str, bytes) nan = np.nan if all(isinstance(x, string_types) or x is nan for x in data): # Force any missing (null) values to b''. Numpy will # upcast to str/unicode as needed. We go via a list to # avoid replacing objects in a view of the pandas array and # to ensure numpy initializes to string or bytes correctly. data = np.array([b"" if m else d for (d, m) in zip(data, mask)]) # Numpy datetime64 if data.dtype.kind == "M": from astropy.time import Time out[name] = Time(data, format="datetime64") if np.any(mask): out[name][mask] = np.ma.masked out[name].format = "isot" # Numpy timedelta64 elif data.dtype.kind == "m": from astropy.time import TimeDelta data_sec = data.astype("timedelta64[ns]").astype(np.float64) / 1e9 out[name] = TimeDelta(data_sec, format="sec") if np.any(mask): out[name][mask] = np.ma.masked else: if np.any(mask): out[name] = MaskedColumn(data=data, name=name, mask=mask, unit=unit) else: out[name] = Column(data=data, name=name, unit=unit) return cls(out)
info = TableInfo()
[docs] class QTable(Table): """A class to represent tables of heterogeneous data. `~astropy.table.QTable` provides a class for heterogeneous tabular data which can be easily modified, for instance adding columns or new rows. The `~astropy.table.QTable` class is identical to `~astropy.table.Table` except that columns with an associated ``unit`` attribute are converted to `~astropy.units.Quantity` objects. For more information see: - https://docs.astropy.org/en/stable/table/ - https://docs.astropy.org/en/stable/table/mixin_columns.html Parameters ---------- data : numpy ndarray, dict, list, table-like object, optional Data to initialize table. masked : bool, optional Specify whether the table is masked. names : list, optional Specify column names. dtype : list, optional Specify column data types. meta : dict, optional Metadata associated with the table. copy : bool, optional Copy the input data. If the input is a (Q)Table the ``meta`` is always copied regardless of the ``copy`` parameter. Default is True. rows : numpy ndarray, list of list, optional Row-oriented data for table instead of ``data`` argument. copy_indices : bool, optional Copy any indices in the input data. Default is True. units : list, dict, optional List or dict of units to apply to columns. descriptions : list, dict, optional List or dict of descriptions to apply to columns. **kwargs : dict, optional Additional keyword args when converting table-like object. """ def _is_mixin_for_table(self, col): """ Determine if ``col`` should be added to the table directly as a mixin column. """ return has_info_class(col, MixinInfo) def _convert_col_for_table(self, col): if isinstance(col, Column) and getattr(col, "unit", None) is not None: # We need to turn the column into a quantity; use subok=True to allow # Quantity subclasses identified in the unit (such as u.mag()). q_cls = Masked(Quantity) if isinstance(col, MaskedColumn) else Quantity try: qcol = q_cls(col.data, col.unit, copy=COPY_IF_NEEDED, subok=True) except Exception as exc: warnings.warn( f"column {col.info.name} has a unit but is kept as " f"a {col.__class__.__name__} as an attempt to " f"convert it to Quantity failed with:\n{exc!r}", AstropyUserWarning, ) else: qcol.info = col.info qcol.info.indices = col.info.indices col = qcol else: col = super()._convert_col_for_table(col) return col