Source code for astropy.table.table

# Licensed under a 3-clause BSD style license - see LICENSE.rst
from .index import TableIndices, TableLoc, TableILoc, TableLocIndices

import re
import sys
from collections import OrderedDict
from collections.abc import Mapping
import warnings
from copy import deepcopy

import numpy as np
from numpy import ma

from .. import log
from ..io import registry as io_registry
from ..units import Quantity, QuantityInfo
from ..utils import isiterable, ShapedLikeNDArray
from ..utils.console import color_print
from ..utils.metadata import MetaData
from ..utils.data_info import BaseColumnInfo, MixinInfo, ParentDtypeInfo, DataInfo
from ..utils.exceptions import AstropyDeprecationWarning, NoValue

from . import groups
from .pprint import TableFormatter
from .column import (BaseColumn, Column, MaskedColumn, _auto_names, FalseArray,
                     col_copy)
from .row import Row
from .np_utils import fix_column_name, recarray_fromrecords
from .info import TableInfo, serialize_method_as
from .index import Index, _IndexModeContext, get_index
from . import conf


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


[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. """ pass
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): return hasattr(obj, 'info') and isinstance(obj.info, cls)
[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 self.values()[item] elif (isinstance(item, np.ndarray) and item.shape == () and item.dtype.kind == 'i'): return 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('Illegal key or index value for {} object' .format(self.__class__.__name__)) def __setitem__(self, item, value): if item in self: raise ValueError("Cannot replace column '{0}'. Use Table.replace_column() instead." .format(item)) super().__setitem__(item, value) def __repr__(self): names = ("'{0}'".format(x) for x in self.keys()) return "<{1} names=({0})>".format(",".join(names), self.__class__.__name__) def _rename_column(self, name, new_name): if name == new_name: return 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(self.values()) self.clear() self.update(list(zip(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] def isinstance(self, cls): """ Return a list of columns which are instances of the specified classes. Parameters ---------- cls : class or tuple of classes Column class (including mixin) or tuple of Column classes. Returns ------- col_list : list of Columns 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 of classes Column class (including mixin) or tuple of Column classes. Returns ------- col_list : list of Columns 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
[docs]class Table: """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, Table, or 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 Table the ``meta`` is always copied regardless of the ``copy`` parameter. Default is True. rows : numpy ndarray, list of lists, optional Row-oriented data for table instead of ``data`` argument. copy_indices : bool, optional Copy any indices in the input data. Default is True. **kwargs : dict, optional Additional keyword args when converting table-like object. """ meta = MetaData() # Define class attributes for core container objects to allow for subclass # customization. Row = Row Column = Column MaskedColumn = MaskedColumn TableColumns = TableColumns TableFormatter = TableFormatter
[docs] def as_array(self, keep_byteorder=False): """ 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). Returns ------- table_array : np.ndarray (unmasked) or np.ma.MaskedArray (masked) Copy of table as a numpy structured array """ if len(self.columns) == 0: return None sys_byteorder = ('>', '<')[sys.byteorder == 'little'] native_order = ('=', sys_byteorder) dtype = [] cols = self.columns.values() for col in cols: col_descr = descr(col) byteorder = col.info.dtype.byteorder if not keep_byteorder and byteorder not in native_order: new_dt = np.dtype(col_descr[1]).newbyteorder('=') col_descr = (col_descr[0], new_dt, col_descr[2]) dtype.append(col_descr) empty_init = ma.empty if self.masked else np.empty 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 return data
def __init__(self, data=None, masked=None, names=None, dtype=None, meta=None, copy=True, rows=None, copy_indices=True, **kwargs): # Set up a placeholder empty table self._set_masked(masked) self.columns = self.TableColumns() self.meta = meta 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') # 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 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 (though the meta # will be deep-copied anyway). data = data.__astropy_table__(self.__class__, copy, **kwargs) copy = False elif kwargs: raise TypeError('__init__() got unexpected keyword argument {!r}' .format(list(kwargs.keys())[0])) 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)): 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 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): init_func = self._init_from_table n_cols = len(data.colnames) default_names = data.colnames # don't copy indices if the input Table is in non-copy mode self._init_indices = self._init_indices and data._copy_indices elif data is None: if names is None: if dtype is None: return # Empty table 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('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 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) is not bool: raise TypeError("masked property has not been set to True or False") 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: mask_table = Table([col.mask for col in self.columns.values()], 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 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 self.columns.values()] else: data = self return self.__class__(data, meta=deepcopy(self.meta))
@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, from among SortedArray, BST, FastBST, FastRBT, and SCEngine. If the supplied argument is None (by default), use 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('Cannot create an index on column "{0}", of ' 'type "{1}"'.format(col.info.name, type(col))) index = Index(columns, engine=engine, unique=unique) if not self.indices: self.primary_key = colnames for col in columns: col.info.indices.append(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): """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.as_array().data if self.masked else self.as_array() 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('{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_of_dicts(self, data, names, dtype, n_cols, copy): 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 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, mixin, or any other iterable object. """ if data and all(isinstance(row, dict) for row in data): self._init_from_list_of_dicts(data, names, dtype, n_cols, copy) return # Set self.masked appropriately, then get class to create column instances. self._set_masked_from_cols(data) cols = [] def_names = _auto_names(n_cols) for col, name, def_name, dtype in zip(data, names, def_names, dtype): # Structured ndarray gets viewed as a mixin unless already a valid # mixin class if (isinstance(col, np.ndarray) and len(col.dtype) > 1 and not self._add_as_mixin_column(col)): col = col.view(NdarrayMixin) if isinstance(col, (Column, MaskedColumn)): col = self.ColumnClass(name=(name or col.info.name or def_name), data=col, dtype=dtype, copy=copy, copy_indices=self._init_indices) elif self._add_as_mixin_column(col): # Copy the mixin column attributes if they exist since the copy below # may not get this attribute. if copy: col = col_copy(col, copy_indices=self._init_indices) col.info.name = name or col.info.name or def_name elif isinstance(col, np.ndarray) or isiterable(col): col = self.ColumnClass(name=(name or def_name), data=col, dtype=dtype, copy=copy, copy_indices=self._init_indices) 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)] newdata = data.view(dtype).ravel() columns = self.TableColumns() for name in names: columns[name] = self.ColumnClass(name=name, data=newdata[name]) columns[name].info.parent_table = self self.columns = columns def _init_from_dict(self, data, names, dtype, n_cols, copy): """Initialize table from a dictionary of columns""" # TODO: is this restriction still needed with no ndarray? 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 self.meta.clear() self.meta.update(deepcopy(table.meta)) self.primary_key = table.primary_key cols = list(table.columns.values()) self._init_from_list(cols, names, dtype, n_cols, copy) def _convert_col_for_table(self, col): """ Make sure that all Column objects have correct 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 col.__class__ is not self.ColumnClass and isinstance(col, Column): col = self.ColumnClass(col) # copy attributes and reference data return col def _init_from_cols(self, cols): """Initialize table from a list of Column or mixin objects""" lengths = set(len(col) for col in cols) if len(lengths) != 1: raise ValueError('Inconsistent data column lengths: {0}' .format(lengths)) # Set the table masking self._set_masked_from_cols(cols) # 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) table.meta.clear() table.meta.update(deepcopy(self.meta)) table.primary_key = self.primary_key cols = self.columns.values() newcols = [] for col in cols: col.info._copy_indices = self._copy_indices newcol = col[slice_] if col.info.indices: newcol = col.info.slice_indices(newcol, slice_, len(col)) newcols.append(newcol) col.info._copy_indices = True self._make_table_from_cols(table, newcols) return table @staticmethod def _make_table_from_cols(table, cols): """ Make ``table`` in-place so that it represents the given list of ``cols``. """ colnames = set(col.info.name for col in cols) if None in colnames: raise TypeError('Cannot have None for column name') if len(colnames) != len(cols): raise ValueError('Duplicate column names') columns = table.TableColumns((col.info.name, col) for col in cols) for col in cols: col.info.parent_table = table if table.masked and not hasattr(col, 'mask'): col.mask = FalseArray(col.shape) table.columns = columns
[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('length={0}'.format(len(self))) descr = ' '.join(descr_vals) if html: from ..utils.xml.writer import xml_escape descr = '<i>{0}</i>\n'.format(xml_escape(descr)) else: descr = '<{0}>\n'.format(descr) if tableid is None: tableid = 'table{id}'.format(id=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): return self._base_repr_(html=True, max_width=-1, tableclass=conf.default_notebook_table_class) 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()) def _add_as_mixin_column(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 not Quantity (which gets converted to Column with # unit set). return has_info_class(col, MixinInfo) and not has_info_class(col, QuantityInfo)
[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``. 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 True. 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. """ 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('Length = {0} rows'.format(len(self))) n_header = outs['n_header'] for i, line in enumerate(lines): if i < n_header: color_print(line, 'red') else: print(line)
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] + self.columns.values(), copy=False) else: return self
[docs] 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 <http://getbootstrap.com/css/#tables>`_ for the list of classes. css : string 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 .jsviewer import JSViewer from IPython.display import HTML if tableid is None: tableid = 'table{0}-{1}'.format(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] 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 : string 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 webbrowser import tempfile from .jsviewer import DEFAULT_CSS from urllib.parse import urljoin from urllib.request import pathname2url 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("Browser '{}' not found.".format(browser)) else: br.open(urljoin('file:', pathname2url(path)))
[docs] 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``. 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. """ 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('Length = {0} rows'.format(len(self))) return lines
[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 True. """ 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], meta=deepcopy(self.meta), copy_indices=self._copy_indices) out._groups = groups.TableGroups(out, indices=self.groups._indices, keys=self.groups._keys) 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) 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, str) and item not in self.colnames: NewColumn = self.MaskedColumn if self.masked else self.Column # If value doesn't have a dtype and won't be added as a mixin then # convert to a numpy array. if not hasattr(value, 'dtype') and not self._add_as_mixin_column(value): value = np.asarray(value) # Structured ndarray gets viewed as a mixin (unless already a valid # mixin class). if (isinstance(value, np.ndarray) and len(value.dtype) > 1 and not self._add_as_mixin_column(value)): value = value.view(NdarrayMixin) # 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. name = item # If this is a column-like object that could be added directly to table if isinstance(value, BaseColumn) or self._add_as_mixin_column(value): # If we're setting a new column to a scalar, broadcast it. # (things will fail in _init_from_cols if this doesn't work) if (len(self) > 0 and (getattr(value, 'isscalar', False) or getattr(value, 'shape', None) == () or len(value) == 1)): new_shape = (len(self),) + getattr(value, 'shape', ())[1:] if isinstance(value, np.ndarray): value = np.broadcast_to(value, shape=new_shape, subok=True) elif isinstance(value, ShapedLikeNDArray): value = value._apply(np.broadcast_to, shape=new_shape, subok=True) new_column = col_copy(value) new_column.info.name = name elif len(self) == 0: new_column = NewColumn(value, name=name) else: new_column = NewColumn(name=name, length=len(self), dtype=value.dtype, shape=value.shape[1:], unit=getattr(value, 'unit', None)) new_column[:] = value # Now add new column to the table self.add_columns([new_column], copy=False) 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) or isinstance(item, np.ndarray) or isinstance(item, list) or (isinstance(item, tuple) and # output from np.where 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): import itertools vals = itertools.repeat(value, n_cols) else: # Assume this is an iterable that will work if len(value) != n_cols: raise ValueError('Right side value needs {0} elements (one for each column)' .format(n_cols)) vals = value for col, val in zip(self.columns.values(), vals): col[item] = val else: raise ValueError('Illegal type {0} for table item access' .format(type(item))) 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 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 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())
def __len__(self): if len(self.columns) == 0: return 0 lengths = set(len(col) for col in self.columns.values()) if len(lengths) != 1: len_strs = [' {0} : {1}'.format(name, len(col)) for name, col in self.columns.items()] raise ValueError('Column length mismatch:\n{0}'.format('\n'.join(len_strs))) return lengths.pop()
[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, name=None, rename_duplicate=False, copy=True): """ 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). 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. 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 Add second column named 'b' with rename_duplicate:: >>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3]], names=('a', 'b')) >>> col_b = Column(name='b', data=[1.1, 1.2, 1.3]) >>> t.add_column(col_b, rename_duplicate=True) >>> print(t) a b b_1 --- --- --- 1 0.1 1.1 2 0.2 1.2 3 0.3 1.3 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')) >>> col_c = Column(data=['x', 'y']) >>> t.add_column(col_c) >>> t.add_column(col_c, name='c') >>> col_b = Column(name='b', data=[1.1, 1.2]) >>> t.add_column(col_b, name='d') >>> print(t) a b col2 c d --- --- ---- --- --- 1 0.1 x x 1.1 2 0.2 y y 1.2 To add several columns use add_columns. """ if index is None: index = len(self.columns) if name is not None: name = (name,) self.add_columns([col], [index], name, copy=copy, rename_duplicate=rename_duplicate)
[docs] def add_columns(self, cols, indexes=None, names=None, copy=True, rename_duplicate=False): """ 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). 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. 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 Add second column 'b' and column 'c' with ``rename_duplicate``:: >>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3]], names=('a', 'b')) >>> col_b = Column(name='b', data=[1.1, 1.2, 1.3]) >>> col_c = Column(name='c', data=['x', 'y', 'z']) >>> t.add_columns([col_b, col_c], rename_duplicate=True) >>> print(t) a b b_1 c --- --- --- --- 1 0.1 1.1 x 2 0.2 1.2 y 3 0.3 1.3 z 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_a = Column(data=['x', 'y']) >>> col_b = Column(name='b', data=['u', 'v']) >>> t.add_columns([col_a, col_b]) >>> t.add_columns([col_a, col_b], names=['c', 'd']) >>> print(t) col0 b c d ---- --- --- --- x u x u y v y 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 copy: cols = [col_copy(col) for col in cols] if len(self.columns) == 0: # 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) if names is None: names = (None,) * len(cols) elif len(names) != len(cols): raise ValueError('Number of names must match number of cols') for i, (col, name) in enumerate(zip(cols, names)): if name is None: if col.info.name is not None: continue name = 'col{}'.format(i + len(self.columns)) if col.info.parent_table is not None: col = col_copy(col) col.info.name = name if rename_duplicate: existing_names = set(self.colnames) for col in cols: i = 1 orig_name = col.info.name if col.info.name in existing_names: # If the column belongs to another table then copy it # before renaming while col.info.name in existing_names: # Iterate until a unique name is found if col.info.parent_table is not None: col = col_copy(col) new_name = '{0}_{1}'.format(orig_name, i) col.info.name = new_name i += 1 existing_names.add(new_name) self._init_from_cols(newcols)
def _replace_column_warnings(self, name, col): """ Same as replace_column but issues warnings under various circumstances. """ warns = conf.replace_warnings if 'refcount' in warns and name in self.colnames: 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("replaced column '{}'".format(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 = ("replaced column '{}' which looks like an array slice. " "The new column no longer shares memory with the " "original array.".format(name)) warnings.warn(msg, TableReplaceWarning, stacklevel=3) except AttributeError: pass if 'refcount' in warns: # Did reference count change? new_refcount = sys.getrefcount(self[name]) if refcount != new_refcount: msg = ("replaced column '{}' and the number of references " "to the column changed.".format(name)) 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 = ("replaced column '{}' and column attributes {} changed." .format(name, changed_attrs)) warnings.warn(msg, TableReplaceWarning, stacklevel=3)
[docs] def replace_column(self, name, col): """ Replace column ``name`` with the new ``col`` object. Parameters ---------- name : str Name of column to replace col : column object (list, ndarray, Column, etc) New column object to replace the existing column 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('column name {0} is not in the table'.format(name)) if self[name].info.indices: raise ValueError('cannot replace a table index column') t = self.__class__([col], names=[name]) cols = OrderedDict(self.columns) cols[name] = t[name] self._init_from_cols(cols.values())
[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, 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 """ # 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 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, str): 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)
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 - set(['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 : 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, str): 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 TODO: this won't work for mixins 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].info.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("Index {0} is out of bounds for table with length {1}" .format(index, N)) if index < 0: index += N 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) if mask is not None and not self.masked: # Possibly issue upgrade warning and update self.ColumnClass. This # does not change the existing columns. self._set_masked(True) if _is_mapping(vals) 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 _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 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("Value must be supplied for column '{0}'".format(name)) vals = vals_list mask = mask_list if 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 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') columns = self.TableColumns() try: # Insert val at index for each column for name, col, val, mask_ in zip(colnames, self.columns.values(), vals, mask): # If the new row caused a change in self.ColumnClass then # Column-based classes need to be converted first. This is # typical for adding a row with mask values to an unmasked table. if isinstance(col, Column) and not isinstance(col, self.ColumnClass): col = self.ColumnClass(col, copy=False) newcol = col.insert(index, val, axis=0) if not isinstance(newcol, BaseColumn): newcol.info.name = name if self.masked: newcol.mask = FalseArray(newcol.shape) if len(newcol) != N + 1: raise ValueError('Incorrect length for column {0} after inserting {1}' ' (expected {2}, got {3})' .format(name, val, len(newcol), N + 1)) newcol.info.parent_table = self # Set mask if needed if self.masked: newcol.mask[index] = mask_ columns[name] = newcol # insert row in indices for table_index in self.indices: table_index.insert_row(index, vals, self.columns.values()) except Exception as err: raise ValueError("Unable to insert row because of exception in column '{0}':\n{1}" .format(name, err)) else: 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 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, str): keys = [keys] # use index sorted order if possible if keys is not None: index = get_index(self, self[keys]) if index is not None: return index.sorted_data() kwargs = {} if keys: kwargs['order'] = keys if kind: kwargs['kind'] = kind if keys: data = self[keys].as_array() else: data = self.as_array() return data.argsort(**kwargs)
[docs] def sort(self, keys=None): ''' 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. 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 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) sort_index = get_index(self, self[keys]) if sort_index is not None: # avoid inefficient relabelling of sorted index prev_frozen = sort_index._frozen sort_index._frozen = True for col in self.columns.values(): col[:] = col.take(indexes, axis=0) if sort_index is not None: # undo index freeze sort_index._frozen = prev_frozen # now relabel the sort index appropriately sort_index.sort()
[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(): col[:] = col[::-1] for index in self.indices: index.reverse()
[docs] @classmethod 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') See http://docs.astropy.org/en/stable/io/unified.html for details. Parameters ---------- format : str File format specifier. *args : tuple, optional Positional arguments passed through to data reader. If supplied the first argument is the input filename. **kwargs : dict, optional Keyword arguments passed through to data reader. Returns ------- out : `Table` Table corresponding to file contents Notes ----- """ # The hanging Notes section just above is a section placeholder for # import-time processing that collects available formats into an # RST table and inserts at the end of the docstring. DO NOT REMOVE. out = io_registry.read(cls, *args, **kwargs) # For some readers (e.g., ascii.ecsv), the returned `out` class is not # guaranteed to be the same as the desired output `cls`. If so, # try coercing to desired class without copying (io.registry.read # would normally do a copy). The normal case here is swapping # Table <=> QTable. if cls is not out.__class__: try: out = cls(out, copy=False) except Exception: raise TypeError('could not convert reader output to {0} ' 'class.'.format(cls.__name__)) return out
[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') See http://docs.astropy.org/en/stable/io/unified.html for details. Parameters ---------- format : str File format specifier. serialize_method : str, dict, optional Serialization method specifier for columns. *args : tuple, optional Positional arguments passed through to data writer. If supplied the first argument is the output filename. **kwargs : dict, optional Keyword arguments passed through to data writer. Notes ----- """ serialize_method = kwargs.pop('serialize_method', None) with serialize_method_as(self, serialize_method): 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. The ``meta`` is always deepcopied regardless of the value for ``copy_data``. ''' 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): return super().__lt__(other) def __gt__(self, other): return super().__gt__(other) def __le__(self, other): return super().__le__(other) def __ge__(self, other): return super().__ge__(other) def __eq__(self, other): if isinstance(other, Table): other = other.as_array() if self.masked: if isinstance(other, np.ma.MaskedArray): result = self.as_array() == 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.as_array().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.as_array() == other.data) & (other.mask == false_mask) else: result = self.as_array() == 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)
[docs] def to_pandas(self): """ Return a :class:`pandas.DataFrame` instance Returns ------- dataframe : :class:`pandas.DataFrame` A pandas :class:`pandas.DataFrame` instance Raises ------ ImportError If pandas is not installed ValueError If the Table contains mixin or multi-dimensional columns """ from pandas import DataFrame if self.has_mixin_columns: raise ValueError("Cannot convert a table with mixin columns to a pandas DataFrame") if any(getattr(col, 'ndim', 1) > 1 for col in self.columns.values()): raise ValueError("Cannot convert a table with multi-dimensional columns to a pandas DataFrame") out = OrderedDict() for name, column in self.columns.items(): if isinstance(column, MaskedColumn) and np.any(column.mask): if column.dtype.kind in ['i', 'u']: out[name] = column.astype(float).filled(np.nan) warnings.warn( "converted column '{}' from integer to float".format( name), TableReplaceWarning, stacklevel=3) elif column.dtype.kind in ['f', 'c']: out[name] = column.filled(np.nan) else: out[name] = column.astype(object).filled(np.nan) else: out[name] = column if out[name].dtype.byteorder not in ('=', '|'): out[name] = out[name].byteswap().newbyteorder() return DataFrame(out)
[docs] @classmethod def from_pandas(cls, dataframe): """ Create a `Table` from a :class:`pandas.DataFrame` instance Parameters ---------- dataframe : :class:`pandas.DataFrame` The pandas :class:`pandas.DataFrame` instance Returns ------- table : `Table` A `Table` (or subclass) instance """ out = OrderedDict() for name in dataframe.columns: column = dataframe[name] mask = np.array(column.isnull()) data = np.array(column) 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. data[mask] = b'' # When the numpy object array is represented as a list then # numpy initializes to the correct string or unicode type. data = np.array([x for x in data]) if np.any(mask): out[name] = MaskedColumn(data=data, name=name, mask=mask) else: out[name] = Column(data=data, name=name) return cls(out)
info = TableInfo()
[docs]class QTable(Table): """A class to represent tables of heterogeneous data. `QTable` provides a class for heterogeneous tabular data which can be easily modified, for instance adding columns or new rows. The `QTable` class is identical to `Table` except that columns with an associated ``unit`` attribute are converted to `~astropy.units.Quantity` objects. Parameters ---------- data : numpy ndarray, dict, list, Table, or 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. Default is True. rows : numpy ndarray, list of lists, optional Row-oriented data for table instead of ``data`` argument. copy_indices : bool, optional Copy any indices in the input data. Default is True. **kwargs : dict, optional Additional keyword args when converting table-like object. """ def _add_as_mixin_column(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, or a subclass # identified in the unit (such as u.mag()). q_cls = getattr(col.unit, '_quantity_class', Quantity) qcol = q_cls(col.data, col.unit, copy=False) qcol.info = col.info col = qcol else: col = super()._convert_col_for_table(col) return col
[docs]class NdarrayMixin(np.ndarray): """ Mixin column class to allow storage of arbitrary numpy ndarrays within a Table. This is a subclass of numpy.ndarray and has the same initialization options as ndarray(). """ info = ParentDtypeInfo() def __new__(cls, obj, *args, **kwargs): self = np.array(obj, *args, **kwargs).view(cls) if 'info' in getattr(obj, '__dict__', ()): self.info = obj.info return self def __array_finalize__(self, obj): if obj is None: return if callable(super().__array_finalize__): super().__array_finalize__(obj) # Self was created from template (e.g. obj[slice] or (obj * 2)) # or viewcast e.g. obj.view(Column). In either case we want to # init Column attributes for self from obj if possible. if 'info' in getattr(obj, '__dict__', ()): self.info = obj.info def __reduce__(self): # patch to pickle Quantity objects (ndarray subclasses), see # http://www.mail-archive.com/numpy-discussion@scipy.org/msg02446.html object_state = list(super().__reduce__()) object_state[2] = (object_state[2], self.__dict__) return tuple(object_state) def __setstate__(self, state): # patch to unpickle NdarrayMixin objects (ndarray subclasses), see # http://www.mail-archive.com/numpy-discussion@scipy.org/msg02446.html nd_state, own_state = state super().__setstate__(nd_state) self.__dict__.update(own_state)