BinnedTimeSeries#

class astropy.timeseries.BinnedTimeSeries(data=None, *, time_bin_start=None, time_bin_end=None, time_bin_size=None, n_bins=None, **kwargs)[source]#

Bases: BaseTimeSeries

A class to represent binned time series data in tabular form.

BinnedTimeSeries provides a class for representing time series as a collection of values of different quantities measured in time bins (for time series with values sampled at specific times, see the TimeSeries class). BinnedTimeSeries is a sub-class of QTable and thus provides all the standard table maniplation methods available to tables, but it also provides additional conveniences for dealing with time series, such as a flexible initializer for setting up the times, and attributes to access the start/center/end time of bins.

Parameters:

datanumpy ndarray, dict, list, astropy:table-like object, optional: Data to initialize time series. This does not need to contain the times, which can be provided separately, but if it does contain the times they should be in columns called 'time_bin_start' and 'time_bin_size' to be automatically recognized.
time_bin_startTime or iterable: The times of the start of each bin - this can be either given directly as a Time array or as any iterable that initializes the Time class. If this is given, then the remaining time-related arguments should not be used. This can also be a scalar value if time_bin_size is provided.
time_bin_endTime or iterable: The times of the end of each bin - this can be either given directly as a Time array or as any value or iterable that initializes the Time class. If this is given, then the remaining time-related arguments should not be used. This can only be given if time_bin_start is an array of values. If time_bin_end is a scalar, time bins are assumed to be contiguous, such that the end of each bin is the start of the next one, and time_bin_end gives the end time for the last bin. If time_bin_end is an array, the time bins do not need to be contiguous. If this argument is provided, time_bin_size should not be provided.
time_bin_sizeTimeDelta or Quantity: The size of the time bins, either as a scalar value (in which case all time bins will be assumed to have the same duration) or as an array of values (in which case each time bin can have a different duration). If this argument is provided, time_bin_end should not be provided.
n_binsint: The number of time bins for the series. This is only used if both time_bin_start and time_bin_size are provided and are scalar values.
**kwargsdict, optional: Additional keyword arguments are passed to QTable.

Attributes Summary

`ColumnClass`
`colnames`
`dtype`
`groups`
`has_masked_columns`	True if table has any `MaskedColumn` columns.
`has_masked_values`	True if column in the table has values which are masked.
`has_mixin_columns`	True if table has any mixin columns (defined as columns that are not Column subclasses).
`iloc`	Return a TableILoc object that can be used for retrieving indexed rows in the order they appear in the index.
`indices`	Return the indices associated with columns of the table as a TableIndices object.
`info`
`loc`	Return a TableLoc object that can be used for retrieving rows by index in a given data range.
`loc_indices`	Return a TableLocIndices object that can be used for retrieving the row indices corresponding to given table index key value or values.
`mask`
`masked`
`meta`
`pprint_exclude_names`	Maintain tuple that controls table column visibility for print output.
`pprint_include_names`	Maintain tuple that controls table column visibility for print output.
`time_bin_center`	The center times of all the time bins.
`time_bin_end`	The end times of all the time bins.
`time_bin_size`	The sizes of all the time bins.
`time_bin_start`	The start times of all the time bins.
`write`	Write this Table object out in the specified format.

Methods Summary

`add_column`(col[, index, name, ...])	Add a new column to the table using `col` as input.
`add_columns`(cols[, indexes, names, copy, ...])	Add a list of new columns the table using `cols` data objects.
`add_index`(colnames[, engine, unique])	Insert a new index among one or more columns.
`add_row`([vals, mask])	Add a new row to the end of the table.
`argsort`([keys, kind, reverse])	Return the indices which would sort the table according to one or more key columns.
`as_array`([keep_byteorder, names])	Return a new copy of the table in the form of a structured np.ndarray or np.ma.MaskedArray object (as appropriate).
`convert_bytestring_to_unicode`()	Convert bytestring columns (dtype.kind='S') to unicode (dtype.kind='U') using UTF-8 encoding.
`convert_unicode_to_bytestring`()	Convert unicode columns (dtype.kind='U') to bytestring (dtype.kind='S') using UTF-8 encoding.
`copy`([copy_data])	Return a copy of the table.
`field`(item)	Return column[item] for recarray compatibility.
`filled`([fill_value])	Return copy of self, with masked values filled.
`from_pandas`(dataframe[, index, units])	Create a `Table` from a `pandas.DataFrame` instance.
`group_by`(keys)	Group this table by the specified `keys`.
`index_column`(name)	Return the positional index of column `name`.
`index_mode`(mode)	Return a context manager for an indexing mode.
`insert_row`(index[, vals, mask])	Add a new row before the given `index` position in the table.
`items`()
`itercols`()	Iterate over the columns of this table.
`iterrows`(*names)	Iterate over rows of table returning a tuple of values for each row.
`keep_columns`(names)	Keep only the columns specified (remove the others).
`keys`()
`more`([max_lines, max_width, show_name, ...])	Interactively browse table with a paging interface.
`pformat`([max_lines, max_width, show_name, ...])	Return a list of lines for the formatted string representation of
`pformat_all`([max_lines, max_width, ...])	Return a list of lines for the formatted string representation of
`pprint`([max_lines, max_width, show_name, ...])	Print a formatted string representation of the table.
`pprint_all`([max_lines, max_width, ...])	Print a formatted string representation of the entire table.
`read`(filename[, time_bin_start_column, ...])	Read and parse a file and returns a `astropy.timeseries.BinnedTimeSeries`.
`remove_column`(name)	Remove a column from the table.
`remove_columns`(names)	Remove several columns from the table.
`remove_indices`(colname)	Remove all indices involving the given column.
`remove_row`(index)	Remove a row from the table.
`remove_rows`(row_specifier)	Remove rows from the table.
`rename_column`(name, new_name)	Rename a column.
`rename_columns`(names, new_names)	Rename multiple columns.
`replace_column`(name, col[, copy])	Replace column `name` with the new `col` object.
`reverse`()	Reverse the row order of table rows.
`round`([decimals])	Round numeric columns in-place to the specified number of decimals.
`show_in_browser`([max_lines, jsviewer, ...])	Render the table in HTML and show it in a web browser.
`show_in_notebook`([tableid, css, ...])	Render the table in HTML and show it in the IPython notebook.
`sort`([keys, kind, reverse])	Sort the table according to one or more keys.
`to_pandas`([index, use_nullable_int])	Return a `pandas.DataFrame` instance.
`update`(other[, copy])	Perform a dictionary-style update and merge metadata.
`values`()
`values_equal`(other)	Element-wise comparison of table with another table, list, or scalar.

Attributes Documentation

ColumnClass#

colnames#

dtype#

groups#

has_masked_columns#

True if table has any MaskedColumn columns.

This does not check for mixin columns that may have masked values, use the has_masked_values property in that case.

has_masked_values#

True if column in the table has values which are masked.

This may be relatively slow for large tables as it requires checking the mask values of each column.

has_mixin_columns#: True if table has any mixin columns (defined as columns that are not Column subclasses).

iloc#: Return a TableILoc object that can be used for retrieving indexed rows in the order they appear in the index.

indices#: Return the indices associated with columns of the table as a TableIndices object.

info#

loc#: 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.

loc_indices#: Return a TableLocIndices object that can be used for retrieving the row indices corresponding to given table index key value or values.

mask#

masked#

meta = None#

pprint_exclude_names#

Maintain tuple that controls table column visibility for print output.

This is a descriptor that inherits from MetaAttribute so that the attribute value is stored in the table meta[‘__attributes__’].

This gets used for the pprint_include_names and pprint_exclude_names Table attributes.

pprint_include_names#

Maintain tuple that controls table column visibility for print output.

This is a descriptor that inherits from MetaAttribute so that the attribute value is stored in the table meta[‘__attributes__’].

This gets used for the pprint_include_names and pprint_exclude_names Table attributes.

time_bin_center#: The center times of all the time bins.

time_bin_end#: The end times of all the time bins.

time_bin_size#: The sizes of all the time bins.

time_bin_start#: The start times of all the time bins.

write#

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')

Get help on the available writers for Table using the``help()`` method:

>>> Table.write.help()  # Get help writing Table and list supported formats
>>> Table.write.help('fits')  # Get detailed help on Table FITS writer
>>> Table.write.list_formats()  # Print list of available formats

The serialize_method argument is explained in the section on Table serialization methods.

Parameters:

*argstuple, optional: Positional arguments passed through to data writer. If supplied the first argument is the output filename.
formatstr: File format specifier.
serialize_methodstr, dict, optional: Serialization method specifier for columns.
**kwargsdict, optional: Keyword arguments passed through to data writer.

Methods Documentation

add_column(col, index=None, name=None, rename_duplicate=False, copy=True, default_name=None)#

Add a new column to the table using col as input. If index is supplied then insert column before index position in the list of columns, otherwise append column to the end of the list.

The col input can be any data object which is acceptable as a Table column object or can be converted. This includes mixin columns and scalar or length=1 objects which get broadcast to match the table length.

To add several columns at once use add_columns() or simply call add_column() for each one. There is very little performance difference in the two approaches.

Parameters:

colobject: Data object for the new column
indexint or None: Insert column before this position or at end (default).
namestr: Column name
rename_duplicatebool: Uniquify column name if it already exist. Default is False.
copybool: Make a copy of the new column. Default is True.
default_namestr or None: Name to use if both name and col.info.name are not available. Defaults to col{number_of_columns}.

Examples

Create a table with two columns ‘a’ and ‘b’, then create a third column ‘c’ and append it to the end of the table:

>>> t = Table([[1, 2], [0.1, 0.2]], names=('a', 'b'))
>>> col_c = Column(name='c', data=['x', 'y'])
>>> t.add_column(col_c)
>>> print(t)
 a   b   c
--- --- ---
  1 0.1   x
  2 0.2   y

Add column ‘d’ at position 1. Note that the column is inserted before the given index:

>>> t.add_column(['a', 'b'], name='d', index=1)
>>> print(t)
 a   d   b   c
--- --- --- ---
  1   a 0.1   x
  2   b 0.2   y

Add second column named ‘b’ with rename_duplicate:

>>> t = Table([[1, 2], [0.1, 0.2]], names=('a', 'b'))
>>> t.add_column(1.1, name='b', rename_duplicate=True)
>>> print(t)
 a   b  b_1
--- --- ---
  1 0.1 1.1
  2 0.2 1.1

Add an unnamed column or mixin object in the table using a default name or by specifying an explicit name with name. Name can also be overridden:

>>> t = Table([[1, 2], [0.1, 0.2]], names=('a', 'b'))
>>> t.add_column(['a', 'b'])
>>> t.add_column(col_c, name='d')
>>> print(t)
 a   b  col2  d
--- --- ---- ---
  1 0.1    a   x
  2 0.2    b   y

add_columns(cols, indexes=None, names=None, copy=True, rename_duplicate=False)#

Add a list of new columns the table using cols data objects. If a corresponding list of indexes is supplied then insert column before each index position in the original list of columns, otherwise append columns to the end of the list.

The cols input can include any data objects which are acceptable as Table column objects or can be converted. This includes mixin columns and scalar or length=1 objects which get broadcast to match the table length.

From a performance perspective there is little difference between calling this method once or looping over the new columns and calling add_column() for each column.

Parameters:

colslist of object: List of data objects for the new columns
indexeslist of int or None: Insert column before this position or at end (default).
nameslist of str: Column names
copybool: Make a copy of the new columns. Default is True.
rename_duplicatebool: Uniquify new column names if they duplicate the existing ones. Default is False.

See also

astropy.table.hstack, update, replace_column

Examples

Create a table with two columns ‘a’ and ‘b’, then create columns ‘c’ and ‘d’ and append them to the end of the table:

>>> t = Table([[1, 2], [0.1, 0.2]], names=('a', 'b'))
>>> col_c = Column(name='c', data=['x', 'y'])
>>> col_d = Column(name='d', data=['u', 'v'])
>>> t.add_columns([col_c, col_d])
>>> print(t)
 a   b   c   d
--- --- --- ---
  1 0.1   x   u
  2 0.2   y   v

Add column ‘c’ at position 0 and column ‘d’ at position 1. Note that the columns are inserted before the given position:

>>> t = Table([[1, 2], [0.1, 0.2]], names=('a', 'b'))
>>> t.add_columns([['x', 'y'], ['u', 'v']], names=['c', 'd'],
...               indexes=[0, 1])
>>> print(t)
 c   a   d   b
--- --- --- ---
  x   1   u 0.1
  y   2   v 0.2

Add second column ‘b’ and column ‘c’ with rename_duplicate:

>>> t = Table([[1, 2], [0.1, 0.2]], names=('a', 'b'))
>>> t.add_columns([[1.1, 1.2], ['x', 'y']], names=('b', 'c'),
...               rename_duplicate=True)
>>> print(t)
 a   b  b_1  c
--- --- --- ---
  1 0.1 1.1  x
  2 0.2 1.2  y

Add unnamed columns or mixin objects in the table using default names or by specifying explicit names with names. Names can also be overridden:

>>> t = Table()
>>> col_b = Column(name='b', data=['u', 'v'])
>>> t.add_columns([[1, 2], col_b])
>>> t.add_columns([[3, 4], col_b], names=['c', 'd'])
>>> print(t)
col0  b   c   d
---- --- --- ---
   1   u   3   u
   2   v   4   v

add_index(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:

colnamesstr or list: List of column names (or a single column name) to index
enginetype or None: Indexing engine class to use, either SortedArray, BST, or SCEngine. If the supplied argument is None (by default), use SortedArray.
uniquebool: Whether the values of the index must be unique. Default is False.

add_row(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:

valstuple, list, dict or None: Use the specified values in the new row
masktuple, 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

argsort(keys=None, kind=None, reverse=False)#

Return the indices which would sort the table according to one or more key columns. This simply calls the numpy.argsort function on the table with the order parameter set to keys.

Parameters:

keysstr or list of str: The column name(s) to order the table by
kind{‘quicksort’, ‘mergesort’, ‘heapsort’, ‘stable’}, optional: Sorting algorithm used by numpy.argsort.
reversebool: Sort in reverse order (default=False)

Returns:

index_arrayndarray, int: Array of indices that sorts the table by the specified key column(s).

as_array(keep_byteorder=False, names=None)#

Return a new copy of the table in the form of a structured np.ndarray or np.ma.MaskedArray object (as appropriate).

Parameters:

keep_byteorderbool, 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).
nameslist, optional:: List of column names to include for returned structured array. Default is to include all table columns.

Returns:

table_arrayarray or MaskedArray: Copy of table as a numpy structured array. ndarray for unmasked or MaskedArray for masked.

convert_bytestring_to_unicode()#

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.

convert_unicode_to_bytestring()#

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.

copy(copy_data=True)#

Return a copy of the table.

Parameters:

copy_databool: 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.

field(item)#: Return column[item] for recarray compatibility.

filled(fill_value=None)#

Return copy of self, with masked values filled.

If input fill_value supplied then that value is used for all masked entries in the table. Otherwise the individual fill_value defined for each table column is used.

Parameters:

fill_valuestr: If supplied, this fill_value is used for all masked entries in the entire table.

Returns:

filled_tableTable: New table with masked values filled

classmethod from_pandas(dataframe, index=False, units=None)#

Create a Table from a pandas.DataFrame instance.

In addition to converting generic numeric or string columns, this supports conversion of pandas Date and Time delta columns to Time and TimeDelta columns, respectively.

Parameters:

dataframepandas.DataFrame: A pandas pandas.DataFrame instance
indexbool: Include the index column in the returned table (default=False)
units: dict: A dict mapping column names to to a Unit. The columns will have the specified unit in the Table.

Returns:

tableTable: A Table (or subclass) instance

Raises:

ImportError: If pandas is not installed

Examples

Here we convert a pandas.DataFrame instance to a QTable.

>>> import numpy as np
>>> import pandas as pd
>>> from astropy.table import QTable

>>> time = pd.Series(['1998-01-01', '2002-01-01'], dtype='datetime64[ns]')
>>> dt = pd.Series(np.array([1, 300], dtype='timedelta64[s]'))
>>> df = pd.DataFrame({'time': time})
>>> df['dt'] = dt
>>> df['x'] = [3., 4.]
>>> with pd.option_context('display.max_columns', 20):
...     print(df)
        time              dt    x
0 1998-01-01 0 days 00:00:01  3.0
1 2002-01-01 0 days 00:05:00  4.0

>>> QTable.from_pandas(df)
<QTable length=2>
          time              dt       x
          Time          TimeDelta float64
----------------------- --------- -------
1998-01-01T00:00:00.000       1.0     3.0
2002-01-01T00:00:00.000     300.0     4.0

group_by(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:

keysstr, list of str, numpy array, or Table: Key grouping object

Returns:

outTable: New table with groups set

index_column(name)#

Return the positional index of column name.

Parameters:

namestr: column name

Returns:

indexint: 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

index_mode(mode)#

Return a context manager for an indexing mode.

Parameters:

modestr: 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.

insert_row(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.

Parameters:

valstuple, list, dict or None: Use the specified values in the new row
masktuple, list, dict or None: Use the specified mask values in the new row

items()#

itercols()#

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.

iterrows(*names)#

Iterate over rows of table returning a tuple of values for each row.

This method is especially useful when only a subset of columns are needed.

The iterrows method can be substantially faster than using the standard Table row iteration (e.g. for row in tbl:), since that returns a new ~astropy.table.Row object for each row and accessing a column in that row (e.g. row['col0']) is slower than tuple access.

Parameters:

nameslist: List of column names (default to all columns if no names provided)

Returns:

rowsiterable: Iterator returns tuples of row values

Examples

Create a table with three columns ‘a’, ‘b’ and ‘c’:

>>> t = Table({'a': [1, 2, 3],
...            'b': [1.0, 2.5, 3.0],
...            'c': ['x', 'y', 'z']})

To iterate row-wise using column names:

>>> for a, c in t.iterrows('a', 'c'):
...     print(a, c)
1 x
2 y
3 z

keep_columns(names)#

Keep only the columns specified (remove the others).

Parameters:

namesstr or iterable of str: The columns to keep. All other columns will be removed.

Examples

Create a table with three columns ‘a’, ‘b’ and ‘c’:

>>> t = Table([[1, 2, 3],[0.1, 0.2, 0.3],['x', 'y', 'z']],
...           names=('a', 'b', 'c'))
>>> print(t)
 a   b   c
--- --- ---
  1 0.1   x
  2 0.2   y
  3 0.3   z

Keep only column ‘a’ of the table:

>>> t.keep_columns('a')
>>> print(t)
 a
---
  1
  2
  3

Keep columns ‘a’ and ‘c’ of the table:

>>> t = Table([[1, 2, 3],[0.1, 0.2, 0.3],['x', 'y', 'z']],
...           names=('a', 'b', 'c'))
>>> t.keep_columns(['a', 'c'])
>>> print(t)
 a   c
--- ---
  1   x
  2   y
  3   z

keys()#

more(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_linesint: Maximum number of lines in table output
max_widthint or None: Maximum character width of output
show_namebool: Include a header row for column names. Default is True.
show_unitbool: 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_dtypebool: Include a header row for column dtypes. Default is False.

pformat(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_linesint or None: Maximum number of rows to output
max_widthint or None: Maximum character width of output
show_namebool: Include a header row for column names. Default is True.
show_unitbool: 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_dtypebool: Include a header row for column dtypes. Default is True.
htmlbool: Format the output as an HTML table. Default is False.
tableidstr 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)
alignstr 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.
tableclassstr or list of str or None: CSS classes for the table; only used if html is set. Default is None.

Returns:

lineslist: Formatted table as a list of strings.

pformat_all(max_lines=-1, max_width=-1, show_name=True, show_unit=None, show_dtype=False, html=False, tableid=None, align=None, tableclass=None)#

Return a list of lines for the formatted string representation of

the entire table.

The same applies for max_width except the configuration item is astropy.conf.max_width.

Parameters:

max_linesint or None: Maximum number of rows to output
max_widthint or None: Maximum character width of output
show_namebool: Include a header row for column names. Default is True.
show_unitbool: 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_dtypebool: Include a header row for column dtypes. Default is True.
htmlbool: Format the output as an HTML table. Default is False.
tableidstr 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)
alignstr 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.
tableclassstr or list of str or None: CSS classes for the table; only used if html is set. Default is None.

Returns:

lineslist: Formatted table as a list of strings.

pprint(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_linesint or None: Maximum number of lines in table output.
max_widthint or None: Maximum character width of output.
show_namebool: Include a header row for column names. Default is True.
show_unitbool: 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_dtypebool: Include a header row for column dtypes. Default is False.
alignstr 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.

pprint_all(max_lines=-1, max_width=-1, show_name=True, show_unit=None, show_dtype=False, align=None)#

Print a formatted string representation of the entire table.

This method is the same as astropy.table.Table.pprint except that the default max_lines and max_width are both -1 so that by default the entire table is printed instead of restricting to the size of the screen terminal.

Parameters:

max_linesint or None: Maximum number of lines in table output.
max_widthint or None: Maximum character width of output.
show_namebool: Include a header row for column names. Default is True.
show_unitbool: 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_dtypebool: Include a header row for column dtypes. Default is False.
alignstr 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.

classmethod read(filename, time_bin_start_column=None, time_bin_end_column=None, time_bin_size_column=None, time_bin_size_unit=None, time_format=None, time_scale=None, format=None, *args, **kwargs)[source]#

Read and parse a file and returns a astropy.timeseries.BinnedTimeSeries.

This method uses the unified I/O infrastructure in Astropy which makes it easy to define readers/writers for various classes (https://docs.astropy.org/en/stable/io/unified.html). By default, this method will try and use readers defined specifically for the astropy.timeseries.BinnedTimeSeries class - however, it is also possible to use the format keyword to specify formats defined for the astropy.table.Table class - in this case, you will need to also provide the column names for column containing the start times for the bins, as well as other column names (see the Parameters section below for details):

>>> from astropy.timeseries.binned import BinnedTimeSeries
>>> ts = BinnedTimeSeries.read('binned.dat', format='ascii.ecsv',
...                            time_bin_start_column='date_start',
...                            time_bin_end_column='date_end')  

Parameters:

filenamestr: File to parse.
formatstr: File format specifier.
time_bin_start_columnstr: The name of the column with the start time for each bin.
time_bin_end_columnstr, optional: The name of the column with the end time for each bin. Either this option or time_bin_size_column should be specified.
time_bin_size_columnstr, optional: The name of the column with the size for each bin. Either this option or time_bin_end_column should be specified.
time_bin_size_unitastropy.units.Unit, optional: If time_bin_size_column is specified but does not have a unit set in the table, you can specify the unit manually.
time_formatstr, optional: The time format for the start and end columns.
time_scalestr, optional: The time scale for the start and end columns.
*argstuple, optional: Positional arguments passed through to the data reader.
**kwargsdict, optional: Keyword arguments passed through to the data reader.

Returns:

outastropy.timeseries.binned.BinnedTimeSeries: BinnedTimeSeries corresponding to the file.

remove_column(name)#

Remove a column from the table.

This can also be done with:

del table[name]

Parameters:

namestr: 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.

remove_columns(names)#

Remove several columns from the table.

Parameters:

namesstr or iterable of str: Names of the columns to remove

Examples

Create a table with three columns ‘a’, ‘b’ and ‘c’:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']],
...     names=('a', 'b', 'c'))
>>> print(t)
 a   b   c
--- --- ---
  1 0.1   x
  2 0.2   y
  3 0.3   z

Remove columns ‘b’ and ‘c’ from the table:

>>> t.remove_columns(['b', 'c'])
>>> print(t)
 a
---
  1
  2
  3

Specifying only a single column also works. Remove column ‘b’ from the table:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']],
...     names=('a', 'b', 'c'))
>>> t.remove_columns('b')
>>> print(t)
 a   c
--- ---
  1   x
  2   y
  3   z

This gives the same as using remove_column.

remove_indices(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:

colnamestr: Name of column

remove_row(index)#

Remove a row from the table.

Parameters:

indexint: 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.

remove_rows(row_specifier)#

Remove rows from the table.

Parameters:

row_specifierslice or int or array of int: Specification for rows to remove

Examples

Create a table with three columns ‘a’, ‘b’ and ‘c’:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']],
...           names=('a', 'b', 'c'))
>>> print(t)
 a   b   c
--- --- ---
  1 0.1   x
  2 0.2   y
  3 0.3   z

Remove rows 0 and 2 from the table:

>>> t.remove_rows([0, 2])
>>> print(t)
 a   b   c
--- --- ---
  2 0.2   y

Note that there are no warnings if the slice operator extends outside the data:

>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']],
...           names=('a', 'b', 'c'))
>>> t.remove_rows(slice(10, 20, 1))
>>> print(t)
 a   b   c
--- --- ---
  1 0.1   x
  2 0.2   y
  3 0.3   z

rename_column(name, new_name)#

Rename a column.

This can also be done directly by setting the name attribute of the info property of the column:

table[name].info.name = new_name

Parameters:

namestr: The current name of the column.
new_namestr: 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

rename_columns(names, new_names)#

Rename multiple columns.

Parameters:

nameslist, tuple: A list or tuple of existing column names.
new_nameslist, tuple: A list or tuple of new column names.

Examples

Create a table with three columns ‘a’, ‘b’, ‘c’:

>>> t = Table([[1,2],[3,4],[5,6]], names=('a','b','c'))
>>> print(t)
  a   b   c
 --- --- ---
  1   3   5
  2   4   6

Renaming columns ‘a’ to ‘aa’ and ‘b’ to ‘bb’:

>>> names = ('a','b')
>>> new_names = ('aa','bb')
>>> t.rename_columns(names, new_names)
>>> print(t)
 aa  bb   c
--- --- ---
  1   3   5
  2   4   6

replace_column(name, col, copy=True)#

Replace column name with the new col object.

The behavior of copy for Column objects is: - copy=True: new class instance with a copy of data and deep copy of meta - copy=False: new class instance with same data and a key-only copy of meta

For mixin columns: - copy=True: new class instance with copy of data and deep copy of meta - copy=False: original instance (no copy at all)

Parameters:

namestr: Name of column to replace
colColumn or ndarray or sequence: New column object to replace the existing column.
copybool: Make copy of the input col, default=True