astropy:docs

Source code for astropy.table.table

# Licensed under a 3-clause BSD style license - see LICENSE.rst
from __future__ import (absolute_import, division, print_function,
                        unicode_literals)
from ..extern import six
from ..extern.six.moves import zip as izip
from ..extern.six.moves import range as xrange

import warnings
import re

from copy import deepcopy
from distutils import version

import numpy as np
from numpy import ma

from .. import log
from ..io import registry as io_registry
from ..units import Quantity
from ..utils import OrderedDict, isiterable, deprecated
from ..utils.console import color_print
from ..utils.exceptions import AstropyDeprecationWarning
from ..utils.metadata import MetaData
from . import groups
from .pprint import TableFormatter
from .column import BaseColumn, Column, MaskedColumn, _auto_names
from .row import Row
from .np_utils import fix_column_name, recarray_fromrecords


# Prior to Numpy 1.6.2, there was a bug (in Numpy) that caused
# sorting of structured arrays containing Unicode columns to
# silently fail.
_NUMPY_VERSION = version.LooseVersion(np.__version__)
_BROKEN_UNICODE_TABLE_SORT = _NUMPY_VERSION < version.LooseVersion('1.6.2')


__doctest_skip__ = ['Table.read', 'Table.write',
                    'Table.convert_bytestring_to_unicode',
                    'Table.convert_unicode_to_bytestring',
                    ]


[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)): # check for Columns in the list cols = [((col.name, col) if hasattr(col, 'name') else col) for col in cols] super(TableColumns, self).__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, six.string_types): return OrderedDict.__getitem__(self, item) elif isinstance(item, int): return self.values()[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('Illegal key or index value for {} object' .format(self.__class__.__name__)) def __repr__(self): names = ("'{0}'".format(x) for x in six.iterkeys(self)) return "<{1} names=({0})>".format(",".join(names), self.__class__.__name__) def _rename_column(self, name, new_name): if new_name in self: raise KeyError("Column {0} already exists".format(new_name)) mapper = {name: new_name} new_names = [mapper.get(name, name) for name in self] cols = list(six.itervalues(self)) self.clear() self.update(list(izip(new_names, cols))) # Define keys and values for Python 2 and 3 source compatibility
[docs] def keys(self): return list(OrderedDict.keys(self))
[docs] def values(self): return list(OrderedDict.values(self))
[docs]class Table(object): """A class to represent tables of heterogeneous data. `Table` provides a class for heterogeneous tabular data, making use of a `numpy` structured array internally to store the data values. A key enhancement provided by the `Table` class 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. `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. Parameters ---------- data : numpy ndarray, dict, list, or Table, 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 (default=True). rows : numpy ndarray, list of lists, optional Row-oriented data for table instead of ``data`` argument """ meta = MetaData() # Define class attributes for core container objects to allow for subclass # customization. Row = Row Column = Column MaskedColumn = MaskedColumn TableColumns = TableColumns TableFormatter = TableFormatter def __init__(self, data=None, masked=None, names=None, dtype=None, meta=None, copy=True, rows=None): # Set up a placeholder empty table self._data = None self._set_masked(masked) self.columns = self.TableColumns() self.meta = meta self.formatter = self.TableFormatter() # Must copy if dtype are changing if not copy and dtype is not None: raise ValueError('Cannot specify dtype when copy=False') # 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. is_list_of_dict = False if rows is not None: if data is not None: raise ValueError('Cannot supply both `data` and `rows` values') if all(isinstance(row, dict) for row in rows): is_list_of_dict = True # Avoid doing the all(...) test twice. data = rows elif isinstance(rows, self.Row): data = rows else: rec_data = recarray_fromrecords(rows) data = [rec_data[name] for name in rec_data.dtype.names] # 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 if isinstance(data, self.Row): data = data._table[data._index:data._index + 1] if isinstance(data, (list, tuple)): init_func = self._init_from_list if data and (is_list_of_dict or all(isinstance(row, dict) for row in data)): n_cols = len(data[0]) else: 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 n_cols = data.shape[1] elif isinstance(data, dict): init_func = self._init_from_dict default_names = list(data) n_cols = len(default_names) elif isinstance(data, Table): init_func = self._init_from_table n_cols = len(data.colnames) default_names = data.colnames elif data is None: if names is None: return # Empty table else: init_func = self._init_from_list n_cols = len(names) data = [[]] * n_cols else: raise ValueError('Data type {0} not allowed to init Table' .format(type(data))) # 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 names is None: names = default_names or [None] * n_cols if dtype is None: dtype = [None] * n_cols # Numpy does not support Unicode column names on Python 2, or # bytes column names on Python 3, so fix them up now. names = [fix_column_name(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) # Whatever happens above, the masked property should be set to a boolean if type(self.masked) != bool: raise TypeError("masked property has not been set to True or False") def __getstate__(self): return (self.columns.values(), self.meta) def __setstate__(self, state): columns, meta = state self.__init__(columns, meta=meta) @property def mask(self): return self._data.mask if self.masked else None @mask.setter def mask(self, val): self._data.mask = val @property def _mask(self): """This is needed due to intricacies in numpy.ma, don't remove it.""" return self._data.mask
[docs] def filled(self, fill_value=None): """Return a 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 : Table New table with masked values filled """ if self.masked: data = [col.filled(fill_value) for col in six.itervalues(self.columns)] else: data = self return self.__class__(data, meta=deepcopy(self.meta))
def __array__(self, dtype=None): """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: raise ValueError('Datatype coercion is not allowed') # 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')]) return self._data.data if self.masked else self._data def _rebuild_table_column_views(self): """ Some table manipulations can corrupt the Column views of self._data. This function will cleanly rebuild the columns and self.columns. This is a slightly subtle operation, see comments. """ cols = [] for col in six.itervalues(self.columns): # First make a new column based on the name and the original # column. This step is needed because the table manipulation # may have changed the table masking so that the original data # columns no longer correspond to self.ColumnClass. This uses # data refs, not copies. newcol = self.ColumnClass(name=col.name, data=col) # Now use the copy() method to copy the column and its metadata, # but at the same time set the column data to a view of # self._data[col.name]. Somewhat confusingly in this case # copy() refers to copying the column attributes, but the data # are used by reference. newcol = newcol.copy(data=self._data[col.name]) # Make column aware of the parent table newcol.parent_table = self cols.append(newcol) self.columns = self.TableColumns(cols) def _check_names_dtype(self, names, dtype, n_cols): """Make sure that names and dtype are boths 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('{0} must be a list or None'.format(inp_str)) if len(names) != n_cols or len(dtype) != n_cols: raise ValueError( 'Arguments "names" and "dtype" must match number of columns' .format(inp_str)) def _set_masked_from_cols(self, cols): if self.masked is None: if any(isinstance(col, (MaskedColumn, ma.MaskedArray)) for col in cols): self._set_masked(True) else: self._set_masked(False) elif not self.masked: if any(np.any(col.mask) for col in cols if isinstance(col, (MaskedColumn, ma.MaskedArray))): self._set_masked(True) def _init_from_list(self, data, names, dtype, n_cols, copy): """Initialize table from a list of columns. A column can be a Column object, np.ndarray, or any other iterable object. """ if not copy: raise ValueError('Cannot use copy=False with a list data input') # Set self.masked appropriately, then get class to create column instances. self._set_masked_from_cols(data) cols = [] def_names = _auto_names(n_cols) if data and all(isinstance(row, dict) for row in data): names_from_data = set() for row in data: names_from_data.update(row) cols = {} for name in names_from_data: cols[name] = [] for i, row in enumerate(data): try: cols[name].append(row[name]) except KeyError: raise ValueError('Row {0} has no value for column {1}'.format(i, name)) if all(name is None for name in names): names = sorted(names_from_data) self._init_from_dict(cols, names, dtype, n_cols, copy) return for col, name, def_name, dtype in zip(data, names, def_names, dtype): if isinstance(col, (Column, MaskedColumn)): col = self.ColumnClass(name=(name or col.name), data=col, dtype=dtype) elif isinstance(col, np.ndarray) or isiterable(col): col = self.ColumnClass(name=(name or def_name), data=col, dtype=dtype) else: raise ValueError('Elements in list initialization must be ' 'either Column or list-like') cols.append(col) self._init_from_cols(cols) 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)]) # Set self.masked appropriately, then get class to create column instances. self._set_masked_from_cols(cols) if copy: self._init_from_list(cols, names, dtype, n_cols, copy) else: dtype = [(name, col.dtype, col.shape[1:]) for name, col in zip(names, cols)] self._data = data.view(dtype).ravel() columns = self.TableColumns() for name in names: columns[name] = self.ColumnClass(name=name, data=self._data[name]) columns[name].parent_table = self self.columns = columns def _init_from_dict(self, data, names, dtype, n_cols, copy): """Initialize table from a dictionary of columns""" if not copy: raise ValueError('Cannot use copy=False with a dict data input') data_list = [data[name] for name in names] self._init_from_list(data_list, names, dtype, n_cols, copy) def _init_from_table(self, data, names, dtype, n_cols, copy): """Initialize table from an existing Table object """ table = data # data is really a Table, rename for clarity data_names = table.colnames self.meta.clear() self.meta.update(deepcopy(table.meta)) cols = list(six.itervalues(table.columns)) # Set self.masked appropriately from cols self._set_masked_from_cols(cols) if copy: self._init_from_list(cols, names, dtype, n_cols, copy) else: names = [vals[0] or vals[1] for vals in zip(names, data_names)] dtype = [(name, col.dtype) for name, col in zip(names, cols)] data = table._data.view(dtype) self._update_table_from_cols(self, data, cols, names) def _init_from_cols(self, cols): """Initialize table from a list of Column objects""" lengths = set(len(col.data) for col in cols) if len(lengths) != 1: raise ValueError('Inconsistent data column lengths: {0}' .format(lengths)) self._set_masked_from_cols(cols) cols = [self.ColumnClass(name=col.name, data=col) for col in cols] names = [col.name for col in cols] dtype = [col.descr for col in cols] empty_init = ma.empty if self.masked else np.empty data = empty_init(lengths.pop(), dtype=dtype) for col in cols: data[col.name] = col.data self._update_table_from_cols(self, data, cols, names) def _new_from_slice(self, slice_): """Create a new table as a referenced slice from self.""" table = self.__class__(masked=self.masked) table.meta.clear() table.meta.update(deepcopy(self.meta)) cols = list(six.itervalues(self.columns)) names = [col.name for col in cols] data = self._data[slice_] self._update_table_from_cols(table, data, cols, names) return table @staticmethod def _update_table_from_cols(table, data, cols, names): """Update the existing ``table`` so that it represents the given ``data`` (a structured ndarray) with ``cols`` and ``names``.""" columns = table.TableColumns() table._data = data for name, col in zip(names, cols): newcol = col.copy(data=data[name], copy_data=False) newcol.name = name newcol.parent_table = table columns[name] = newcol table.columns = columns def __repr__(self): names = ("'{0}'".format(x) for x in self.colnames) if any(col.unit for col in self.columns.values()): units = ("{0}".format( col.unit if col.unit is None else '\''+str(col.unit)+'\'') for col in self.columns.values()) s = "<{3} rows={0} names=({1}) units=({4})>\n{2}".format( self.__len__(), ','.join(names), repr(self._data), self.__class__.__name__ ,','.join(units)) else: s = "<{3} rows={0} names=({1})>\n{2}".format( self.__len__(), ','.join(names), repr(self._data), self.__class__.__name__) return s def __unicode__(self): lines, n_header = self.formatter._pformat_table(self) return '\n'.join(lines) if six.PY3: __str__ = __unicode__ def __bytes__(self): return six.text_type(self).encode('utf-8') if six.PY2: __str__ = __bytes__
[docs] def pprint(self, max_lines=None, max_width=None, show_name=True, show_unit=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``. 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=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. """ lines, n_header = self.formatter._pformat_table(self, max_lines, max_width, show_name, show_unit) for i, line in enumerate(lines): if i < n_header: color_print(line, 'red') else: print(line)
[docs] def show_in_browser(self, css="table,th,td,tr,tbody {border: 1px solid black; border-collapse: collapse;}", max_lines=5000, jsviewer=False, jskwargs={}, tableid=None, browser='default'): """ Render the table in HTML and show it in a web browser. In order to make a persistent html file, i.e. one that survives refresh, the returned file object must be kept in memory. Parameters ---------- css : string A valid CSS string declaring the formatting for the table 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 https://datatables.net data table. This allows in-browser searching & sorting. See `JSViewer <http://www.jsviewer.com/>`_ jskwargs : dict Passed to the `JSViewer`_ init. 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). browser : str Any legal browser name, e.g. ``'firefox'``, ``'chrome'``, ``'safari'`` (for mac, you may need to use ``'open -a "/Applications/Google Chrome.app" %s'`` for Chrome). If ``'default'``, will use the system default browser. Returns ------- file : A `~tempfile.NamedTemporaryFile` object pointing to the html file on disk. """ import webbrowser import tempfile from .jsviewer import JSViewer tmp = tempfile.NamedTemporaryFile(suffix='.html') if tableid is None: tableid = 'table{id}'.format(id=id(self)) linelist = self.pformat(html=True, max_width=np.inf, max_lines=max_lines, tableid=tableid) if jsviewer: jsv = JSViewer(**jskwargs) js = jsv.command_line(tableid=tableid) else: js = [] css = ["<style>{0}</style>".format(css)] html = "\n".join(['<!DOCTYPE html>','<html>'] + css + js + linelist + ['</html>']) try: tmp.write(html) except TypeError: tmp.write(html.encode('utf8')) tmp.flush() if browser == 'default': webbrowser.open("file://" + tmp.name) else: webbrowser.get(browser).open("file://" + tmp.name) return tmp
[docs] def pformat(self, max_lines=None, max_width=None, show_name=True, show_unit=None, html=False, tableid=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``. 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=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. html : bool Format the output as an HTML table (default=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) Returns ------- lines : list Formatted table as a list of strings """ lines, n_header = self.formatter._pformat_table(self, max_lines, max_width, show_name, show_unit, html, tableid=tableid) return lines
[docs] def more(self, max_lines=None, max_width=None, show_name=True, show_unit=None): """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=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. """ self.formatter._more_tabcol(self, max_lines, max_width, show_name, show_unit)
def _repr_html_(self): # Since the user cannot provide input, need a sensible default tableid = 'table{id}'.format(id=id(self)) lines = self.pformat(html=True, tableid=tableid, max_width=-1) return ''.join(lines) def __getitem__(self, item): if isinstance(item, six.string_types): return self.columns[item] elif isinstance(item, (int, np.integer)): return self.Row(self, item) elif isinstance(item, (tuple, list)) and all(isinstance(x, six.string_types) for x in item): bad_names = [x for x in item if x not in self.colnames] if bad_names: raise ValueError('Slice name(s) {0} not valid column name(s)' .format(', '.join(bad_names))) out = self.__class__([self[x] for x in item], meta=deepcopy(self.meta)) out._groups = groups.TableGroups(out, indices=self.groups._indices, keys=self.groups._keys) return out elif (isinstance(item, slice) or isinstance(item, np.ndarray) or isinstance(item, 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('Illegal type {0} for table item access' .format(type(item))) 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, six.string_types) and item not in self.colnames: NewColumn = self.MaskedColumn if self.masked else self.Column # Make sure value is an ndarray so we can get the dtype if not isinstance(value, np.ndarray): value = np.asarray(value) # Make new column and assign the value. If the table currently # has no rows (len=0) of the value is already a Column then # define new column directly from value. In the latter case # this allows for propagation of Column metadata. Otherwise # define a new column with the right length and shape and then # set it from value. This allows for broadcasting, e.g. t['a'] # = 1. if isinstance(value, BaseColumn): new_column = value.copy(copy_data=False) new_column.name = item elif len(self) == 0: new_column = NewColumn(name=item, data=value) else: new_column = NewColumn(name=item, length=len(self), dtype=value.dtype, shape=value.shape[1:]) new_column[:] = value if isinstance(value, Quantity): new_column.unit = value.unit # Now add new column to the table self.add_column(new_column) elif isinstance(value, Row): # Value is another row self._data[item] = value.data else: # Otherwise just delegate to the numpy item setter. self._data[item] = value def __delitem__(self, item): if isinstance(item, six.string_types): self.remove_column(item) elif isinstance(item, tuple): self.remove_columns(item) def __iter__(self): self._iter_index = 0 return self def __next__(self): """Python 3 iterator""" if self._iter_index < len(self._data): val = self[self._iter_index] self._iter_index += 1 return val else: raise StopIteration if six.PY2: next = __next__
[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 hasattr(self, '_masked'): # The only allowed change is from None to False or True, or False to True if self._masked is None and masked in [False, True]: self._masked = masked elif self._masked is False and masked is True: log.info("Upgrading Table to masked Table. Use Table.filled() to convert to unmasked table.") self._masked = masked elif self._masked is masked: raise Exception("Masked attribute is already set to {0}".format(masked)) else: raise Exception("Cannot change masked attribute to {0} once it is set to {1}" .format(masked, self._masked)) else: if masked in [True, False, None]: self._masked = masked else: raise ValueError("masked should be one of True, False, None") if self._masked: self._column_class = self.MaskedColumn else: self._column_class = 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 self._data.dtype @property def colnames(self): return list(self.columns.keys())
[docs] def keys(self): return list(self.columns.keys())
def __len__(self): if self._data is None: return 0 else: return len(self._data)
[docs] def create_mask(self): if isinstance(self._data, ma.MaskedArray): raise Exception("data array is already masked") else: self._data = ma.array(self._data)
[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("Column {0} does not exist".format(name))
[docs] def add_column(self, col, index=None): """ Add a new Column object ``col`` to the table. If ``index`` is supplied then insert column before ``index`` position in the list of columns, otherwise append column to the end of the list. Parameters ---------- col : Column Column object to add. index : int or `None` Insert column before this position or at end (default) Examples -------- Create a table with two columns 'a' and 'b':: >>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3]], names=('a', 'b')) >>> print(t) a b --- --- 1 0.1 2 0.2 3 0.3 Create a third column 'c' and append it to the end of the table:: >>> col_c = Column(name='c', data=['x', 'y', 'z']) >>> t.add_column(col_c) >>> print(t) a b c --- --- --- 1 0.1 x 2 0.2 y 3 0.3 z Add column 'd' at position 1. Note that the column is inserted before the given index:: >>> col_d = Column(name='d', data=['a', 'b', 'c']) >>> t.add_column(col_d, 1) >>> print(t) a d b c --- --- --- --- 1 a 0.1 x 2 b 0.2 y 3 c 0.3 z To add several columns use add_columns. """ if index is None: index = len(self.columns) self.add_columns([col], [index])
[docs] def add_columns(self, cols, indexes=None): """ Add a list of new Column objects ``cols`` to the table. 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. Parameters ---------- cols : list of Columns Column objects to add. indexes : list of ints or `None` Insert column before this position or at end (default) Examples -------- Create a table with two columns 'a' and 'b':: >>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3]], names=('a', 'b')) >>> print(t) a b --- --- 1 0.1 2 0.2 3 0.3 Create column 'c' and 'd' and append them to the end of the table:: >>> col_c = Column(name='c', data=['x', 'y', 'z']) >>> col_d = Column(name='d', data=['u', 'v', 'w']) >>> t.add_columns([col_c, col_d]) >>> print(t) a b c d --- --- --- --- 1 0.1 x u 2 0.2 y v 3 0.3 z w 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, 3], [0.1, 0.2, 0.3]], names=('a', 'b')) >>> col_c = Column(name='c', data=['x', 'y', 'z']) >>> col_d = Column(name='d', data=['u', 'v', 'w']) >>> t.add_columns([col_c, col_d], [0, 1]) >>> print(t) c a d b --- --- --- --- x 1 u 0.1 y 2 v 0.2 z 3 w 0.3 """ 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 self._data is None: # No existing table data, init from cols newcols = cols else: newcols = list(self.columns.values()) new_indexes = list(range(len(newcols) + 1)) for col, index in zip(cols, indexes): i = new_indexes.index(index) new_indexes.insert(i, None) newcols.insert(i, col) self._init_from_cols(newcols)
[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, (six.integer_types, 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, int, or array of ints 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 """ try: table = np.delete(self._data, row_specifier, axis=0) except (ValueError, IndexError): # Numpy <= 1.7 raises ValueError while Numpy >= 1.8 raises IndexError raise IndexError('Removing row(s) {0} from table with {1} rows failed' .format(row_specifier, len(self._data))) self._data = table # after updating the row data, the column views will be out of date # and should be updated: self._rebuild_table_column_views() # Revert groups to default (ungrouped) state if hasattr(self, '_groups'): del self._groups
[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 : list A list containing the 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. ''' if isinstance(names, six.string_types): names = [names] for name in names: if name not in self.columns: raise KeyError("Column {0} does not exist".format(name)) for name in names: self.columns.pop(name) newdtype = [(name, self._data.dtype[name]) for name in self._data.dtype.names if name not in names] newdtype = np.dtype(newdtype) if newdtype: if self.masked: table = np.ma.empty(self._data.shape, dtype=newdtype) else: table = np.empty(self._data.shape, dtype=newdtype) for field in newdtype.fields: table[field] = self._data[field] if self.masked: table[field].fill_value = self._data[field].fill_value else: table = None self._data = table
def _convert_string_dtype(self, in_kind, out_kind, python3_only): """ Convert string-like columns to/from bytestring and unicode (internal only). Parameters ---------- in_kind : str Input dtype.kind out_kind : str Output dtype.kind python3_only : bool Only do this operation for Python 3 """ if python3_only and not six.PY3: return # If there are no `in_kind` columns then do nothing cols = self.columns.values() if not any(col.dtype.kind == in_kind for col in cols): return newcols = [] for col in cols: if col.dtype.kind == in_kind: newdtype = re.sub(in_kind, out_kind, col.dtype.str) newcol = col.__class__(col, dtype=newdtype) else: newcol = col newcols.append(newcol) self._init_from_cols(newcols)
[docs] def convert_bytestring_to_unicode(self, python3_only=False): """ Convert bytestring columns (dtype.kind='S') to unicode (dtype.kind='U') assuming ASCII 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 Python 3 scripts to manipulate string arrays with natural syntax. The ``python3_only`` parameter is provided as a convenience so that code can be written in a Python 2 / 3 compatible way:: >>> t = Table.read('my_data.fits') >>> t.convert_bytestring_to_unicode(python3_only=True) Parameters ---------- python3_only : bool Only do this operation for Python 3 """ self._convert_string_dtype('S', 'U', python3_only)
[docs] def convert_unicode_to_bytestring(self, python3_only=False): """ Convert ASCII-only unicode columns (dtype.kind='U') to bytestring (dtype.kind='S'). When exporting a unicode string array to a file in Python 3, it may be desirable to encode unicode columns as bytestrings. This routine takes advantage of numpy automated conversion which works for strings that are pure ASCII. The ``python3_only`` parameter is provided as a convenience so that code can be written in a Python 2 / 3 compatible way:: >>> t.convert_unicode_to_bytestring(python3_only=True) >>> t.write('my_data.fits') Parameters ---------- python3_only : bool Only do this operation for Python 3 """ self._convert_string_dtype('U', 'S', python3_only)
[docs] def keep_columns(self, names): ''' Keep only the columns specified (remove the others). Parameters ---------- names : list A list containing the names of 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 Specifying only a single column name keeps only this column. Keep only column 'a' of the table:: >>> t.keep_columns('a') >>> print(t) a --- 1 2 3 Specifying a list of column names is keeps is also possible. 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 ''' if isinstance(names, six.string_types): names = [names] for name in names: if name not in self.columns: raise KeyError("Column {0} does not exist".format(name)) remove = list(set(self.keys()) - set(names)) self.remove_columns(remove)
[docs] def rename_column(self, name, new_name): ''' Rename a column. This can also be done directly with by setting the ``name`` attribute for a column:: table[name].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("Column {0} does not exist".format(name)) self.columns[name].name = new_name
[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 """ def _is_mapping(obj): """Minimal checker for mapping (dict-like) interface for obj""" attrs = ('__getitem__', '__len__', '__iter__', 'keys', 'values', 'items') return all(hasattr(obj, attr) for attr in attrs) newlen = len(self._data) + 1 if vals is None: vals = np.zeros(1, dtype=self._data.dtype)[0] if mask is not None and not self.masked: self._set_masked(True) # Create a table with one row to test the operation on test_data = (ma.zeros if self.masked else np.zeros)(1, dtype=self._data.dtype) if _is_mapping(vals): if mask is not None and not _is_mapping(mask): 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 self.masked: # We set the mask to True regardless of whether a mask value # is specified or not - that is, any cell where a new row # value is not specified should be treated as missing. test_data.mask[-1] = (True,) * len(test_data.dtype) # First we copy the values for name, val in six.iteritems(vals): try: test_data[name][-1] = val except IndexError: raise ValueError("No column {0} in table".format(name)) if mask: test_data[name].mask[-1] = mask[name] elif isiterable(vals): if mask is not None and (not isiterable(mask) or _is_mapping(mask)): 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 not isinstance(vals, tuple): vals = tuple(vals) test_data[-1] = vals if mask is not None: if len(self.columns) != len(mask): raise ValueError('Mismatch between number of masks and columns') if not isinstance(mask, tuple): mask = tuple(mask) test_data.mask[-1] = mask else: raise TypeError('Vals must be an iterable or mapping or None') # If no errors have been raised, then the table can be resized if self.masked: if newlen == 1: self._data = ma.empty(1, dtype=self._data.dtype) else: self._data = ma.resize(self._data, (newlen,)) else: self._data.resize((newlen,), refcheck=False) # Assign the new row self._data[-1:] = test_data self._rebuild_table_column_views() # Revert groups to default (ungrouped) state if hasattr(self, '_groups'): del self._groups
[docs] def argsort(self, keys=None, kind=None): """ 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'}, optional Sorting algorithm. Returns ------- index_array : ndarray, int Array of indices that sorts the table by the specified key column(s). """ if isinstance(keys, six.string_types): keys = [keys] kwargs = {} if keys: kwargs['order'] = keys if kind: kwargs['kind'] = kind data = self._data if _BROKEN_UNICODE_TABLE_SORT and keys is not None and any( data.dtype[i].kind == 'U' for i in xrange(len(data.dtype))): return np.lexsort([data[key] for key in keys[::-1]]) else: return data.argsort(**kwargs)
[docs] def sort(self, keys): ''' 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 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 ''' if type(keys) is not list: keys = [keys] data = self._data if _BROKEN_UNICODE_TABLE_SORT and any( data.dtype[i].kind == 'U' for i in xrange(len(data.dtype))): # Use an alternate sort implementation that uses argsort ordering = self.argsort(keys=keys) data[:] = data[ordering] else: data.sort(order=keys) self._rebuild_table_column_views()
[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 ''' self._data[:] = self._data[::-1].copy() self._rebuild_table_column_views()
@classmethod
[docs] def read(cls, *args, **kwargs): """ Read and parse a data table and return as a Table. This function provides the Table interface to the astropy unified I/O layer. This allows easily reading a file in many supported data formats using syntax such as:: >>> from astropy.table import Table >>> dat = Table.read('table.dat', format='ascii') >>> events = Table.read('events.fits', format='fits') The arguments and keywords (other than ``format``) provided to this function are passed through to the underlying data reader (e.g. `~astropy.io.ascii.read`). """ return io_registry.read(cls, *args, **kwargs)
[docs] def write(self, *args, **kwargs): """ Write this Table object out in the specified format. This function provides the Table interface to the astropy unified I/O layer. This allows easily writing a file in many supported data formats using syntax such as:: >>> from astropy.table import Table >>> dat = Table([[1, 2], [3, 4]], names=('a', 'b')) >>> dat.write('table.dat', format='ascii') The arguments and keywords (other than ``format``) provided to this function are passed through to the underlying data reader (e.g. `~astropy.io.ascii.write`). """ io_registry.write(self, *args, **kwargs)
[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. Otherwise, use the same data array ''' 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): return self.copy(True) def __copy__(self): return self.copy(False) def __lt__(self, other): if six.PY3: return super(Table, self).__lt__(other) elif six.PY2: raise TypeError("unorderable types: Table() < {0}". format(str(type(other)))) def __gt__(self, other): if six.PY3: return super(Table, self).__gt__(other) elif six.PY2: raise TypeError("unorderable types: Table() > {0}". format(str(type(other)))) def __le__(self, other): if six.PY3: return super(Table, self).__le__(other) elif six.PY2: raise TypeError("unorderable types: Table() <= {0}". format(str(type(other)))) def __ge__(self, other): if six.PY3: return super(Table, self).__ge__(other) else: raise TypeError("unorderable types: Table() >= {0}". format(str(type(other)))) def __eq__(self, other): if isinstance(other, Table): other = other._data if self.masked: if isinstance(other, np.ma.MaskedArray): result = self._data == other else: # 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 self.dtype.names]) result = (self._data.data == other) & (self.mask == false_mask) else: if isinstance(other, np.ma.MaskedArray): # 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 other.dtype.names]) result = (self._data == other.data) & (other.mask == false_mask) else: result = self._data == other return result def __ne__(self, other): return ~self.__eq__(other) @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 `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 - `Table` with same length as this table Parameters ---------- keys : str, list of str, numpy array, or `Table` Key grouping object Returns ------- out : `Table` New table with groups set """ return groups.table_group_by(self, keys)

Page Contents