TimeSeries

class astropy.timeseries.TimeSeries(data=None, *, time=None, time_start=None, time_delta=None, n_samples=None, **kwargs)[source]

Bases: astropy.timeseries.BaseTimeSeries

A class to represent time series data in tabular form.

TimeSeries provides a class for representing time series as a collection of values of different quantities measured at specific points in time (for time series with finite time bins, see the BinnedTimeSeries class). TimeSeries 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, a method for folding time series, and a time attribute for easy access to the time values.

See also: http://docs.astropy.org/en/stable/timeseries/

Parameters:
data : numpy ndarray, dict, list, Table, or 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 a column called 'time' to be automatically recognized.

time : Time or iterable

The times at which the values are sampled - 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.

time_start : Time or str

The time of the first sample in the time series. This is an alternative to providing time and requires that time_delta is also provided.

time_delta : TimeDelta or Quantity

The step size in time for the series. This can either be a scalar if the time series is evenly sampled, or an array of values if it is not.

n_samples : int

The number of time samples for the series. This is only used if both time_start and time_delta are provided and are scalar values.

**kwargs : dict, 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([option, out])
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
time The time values.
write(\*args[, serialize_method])

Methods Summary

add_column(self, \*args, \*\*kwargs) See add_column().
add_columns(self, \*args, \*\*kwargs) See add_columns().
add_index(self, colnames[, engine, unique]) Insert a new index among one or more columns.
add_row(self[, vals, mask]) Add a new row to the end of the table.
argsort(self[, keys, kind, reverse]) Return the indices which would sort the table according to one or more key columns.
as_array(self[, 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(self) Convert bytestring columns (dtype.kind=’S’) to unicode (dtype.kind=’U’) using UTF-8 encoding.
convert_unicode_to_bytestring(self) Convert unicode columns (dtype.kind=’U’) to bytestring (dtype.kind=’S’) using UTF-8 encoding.
copy(self[, copy_data]) Return a copy of the table.
field(self, item) Return column[item] for recarray compatibility.
filled(self[, fill_value]) Return copy of self, with masked values filled.
fold(self[, period, midpoint_epoch]) Return a new TimeSeries folded with a period and midpoint epoch.
from_pandas(df[, time_scale]) Convert a DataFrame to a astropy.timeseries.TimeSeries.
group_by(self, keys) Group this table by the specified keys
index_column(self, name) Return the positional index of column name.
index_mode(self, mode) Return a context manager for an indexing mode.
insert_row(self, index[, vals, mask]) Add a new row before the given index position in the table.
itercols(self) Iterate over the columns of this table.
keep_columns(self, names) Keep only the columns specified (remove the others).
keys(self)
more(self[, max_lines, max_width, …]) Interactively browse table with a paging interface.
pformat(self[, max_lines, max_width, …]) Return a list of lines for the formatted string representation of
pformat_all(self[, max_lines, max_width, …]) Return a list of lines for the formatted string representation of
pprint(self[, max_lines, max_width, …]) Print a formatted string representation of the table.
pprint_all(self[, max_lines, max_width, …]) Print a formatted string representation of the entire table.
read(filename[, time_column, time_format, …]) Read and parse a file and returns a astropy.timeseries.TimeSeries.
remove_column(self, name) Remove a column from the table.
remove_columns(self, names) Remove several columns from the table.
remove_indices(self, colname) Remove all indices involving the given column.
remove_row(self, index) Remove a row from the table.
remove_rows(self, row_specifier) Remove rows from the table.
rename_column(self, name, new_name) Rename a column.
rename_columns(self, names, new_names) Rename multiple columns.
replace_column(self, name, col[, copy]) Replace column name with the new col object.
reverse(self) Reverse the row order of table rows.
show_in_browser(self[, max_lines, jsviewer, …]) Render the table in HTML and show it in a web browser.
show_in_notebook(self[, tableid, css, …]) Render the table in HTML and show it in the IPython notebook.
sort(self[, keys, reverse]) Sort the table according to one or more keys.
to_pandas(self) Convert this TimeSeries to a DataFrame with a DatetimeIndex index.

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(option='attributes', out='')
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
time

The time values.

write(*args, serialize_method=None, **kwargs) = <astropy.table.connect.TableWrite object>

Methods Documentation

add_column(self, *args, **kwargs)[source]

See add_column().

add_columns(self, *args, **kwargs)[source]

See add_columns().

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.

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

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

Parameters:
keys : str or list of str

The column name(s) to order the table by

kind : {‘quicksort’, ‘mergesort’, ‘heapsort’}, optional

Sorting algorithm.

reverse : bool

Sort in reverse order (default=False)

Returns:
index_array : ndarray, int

Array of indices that sorts the table by the specified key column(s).

as_array(self, keep_byteorder=False, names=None)

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

Parameters:
keep_byteorder : bool, optional

By default the returned array has all columns in native byte order. However, if this option is True this preserves the byte order of all columns (if any are non-native).

names : list, optional:

List of column names to include for returned structured array. Default is to include all table columns.

Returns:
table_array : np.ndarray (unmasked) or np.ma.MaskedArray (masked)

Copy of table as a numpy structured array

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.

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.

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.

field(self, item)

Return column[item] for recarray compatibility.

filled(self, fill_value=None)

Return copy of self, with masked values filled.

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

Parameters:
fill_value : str

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

Returns:
filled_table : Table

New table with masked values filled

fold(self, period=None, midpoint_epoch=None)[source]

Return a new TimeSeries folded with a period and midpoint epoch.

Parameters:
period : Quantity

The period to use for folding

midpoint_epoch : Time

The time to use as the midpoint epoch, at which the relative time offset will be 0. Defaults to the first time in the time series.

classmethod from_pandas(df, time_scale='utc')[source]

Convert a DataFrame to a astropy.timeseries.TimeSeries.

Parameters:
df : pandas.DataFrame

A pandas pandas.DataFrame instance.

time_scale : str

The time scale to pass into astropy.time.Time. Defaults to UTC.

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

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
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.

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

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.

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
keys(self)
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.

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.

pformat_all(self, max_lines=-1, max_width=-1, show_name=True, show_unit=None, show_dtype=False, html=False, tableid=None, align=None, tableclass=None)
Return a list of lines for the formatted string representation of

the entire table.

If no value of max_lines is supplied then the height of the screen terminal is used to set max_lines. If the terminal height cannot be determined then the default is taken from the configuration item astropy.conf.max_lines. If a negative value of max_lines is supplied then there is no line limit applied.

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

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.

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 or None

Maximum number of lines in table output.

max_width : int or None

Maximum character width of output.

show_name : bool

Include a header row for column names. Default is True.

show_unit : bool

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

show_dtype : bool

Include a header row for column dtypes. Default is 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.

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

Print a formatted string representation of the entire table.

This method is the same as astropy.table.Table.pprint except that the default max_lines and max_width are both -1 so that by default the entire table is printed instead of restricting to the size of the screen terminal.
Parameters:
max_lines : int or None

Maximum number of lines in table output.

max_width : int or None

Maximum character width of output.

show_name : bool

Include a header row for column names. Default is True.

show_unit : bool

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

show_dtype : bool

Include a header row for column dtypes. Default is 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.

classmethod read(filename, time_column=None, time_format=None, time_scale=None, format=None, *args, **kwargs)[source]

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

This method uses the unified I/O infrastructure in Astropy which makes it easy to define readers/writers for various classes (http://docs.astropy.org/en/stable/io/unified.html). By default, this method will try and use readers defined specifically for the astropy.timeseries.TimeSeries 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 import TimeSeries
>>> ts = TimeSeries.read('sampled.dat', format='ascii.ecsv',
...                      time_column='date')  
Parameters:
filename : str

File to parse.

format : str

File format specifier.

time_column : str, optional

The name of the time column.

time_format : str, optional

The time format for the time column.

time_scale : str, optional

The time scale for the time column.

*args : tuple, optional

Positional arguments passed through to the data reader.

**kwargs : dict, optional

Keyword arguments passed through to the data reader.

Returns:
out : astropy.timeseries.TimeSeries

TimeSeries corresponding to file contents.

Notes

The available built-in formats are:

Format Read Write Auto-identify
kepler.fits Yes No No
tess.fits Yes No No
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.

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.

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

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.

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
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
rename_columns(self, names, new_names)

Rename multiple columns.

Parameters:
names : list, tuple

A list or tuple of existing column names.

new_names : list, tuple

A list or tuple of new column names.

Examples

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

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

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

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

Replace column name with the new col object.

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

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

Parameters:
name : str

Name of column to replace

col : column object (list, ndarray, Column, etc)

New column object to replace the existing column

copy : bool

Make copy of the input col, default=True

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)
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
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 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”.

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 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.

sort(self, keys=None, reverse=False)

Sort the table according to one or more keys. This operates on the existing table and does not return a new table.

Parameters:
keys : str or list of str

The key(s) to order the table by. If None, use the primary index of the Table.

reverse : bool

Sort in reverse order (default=False)

Examples

Create a table with 3 columns:

>>> t = Table([['Max', 'Jo', 'John'], ['Miller', 'Miller', 'Jackson'],
...            [12, 15, 18]], names=('firstname', 'name', 'tel'))
>>> print(t)
firstname   name  tel
--------- ------- ---
      Max  Miller  12
       Jo  Miller  15
     John Jackson  18

Sorting according to standard sorting rules, first ‘name’ then ‘firstname’:

>>> t.sort(['name', 'firstname'])
>>> print(t)
firstname   name  tel
--------- ------- ---
     John Jackson  18
       Jo  Miller  15
      Max  Miller  12

Sorting according to standard sorting rules, first ‘firstname’ then ‘tel’, in reverse order:

>>> t.sort(['firstname', 'tel'], reverse=True)
>>> print(t)
firstname   name  tel
--------- ------- ---
      Max  Miller  12
     John Jackson  18
       Jo  Miller  15
to_pandas(self)[source]

Convert this TimeSeries to a DataFrame with a DatetimeIndex index.

Returns:
dataframe : pandas.DataFrame

A pandas pandas.DataFrame instance