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- Constructing a table

# Constructing a table

There is great deal of flexibility in the way that a table can be initially constructed. Details on the inputs to the `Table` constructor are in the Initialization Details section. However, the easiest way to understand how to make a table is by example.

## Examples

Much of the flexibility lies in the types of data structures which can be used to initialize the table data. The examples below show how to create a table from scratch with no initial data, create a table with a list of columns, a dictionary of columns, or from `numpy` arrays (either structured or homogeneous).

### Setup

For the following examples you need to import the `Table` and `Column` classes along with the `numpy` package:

    >>> from astropy.table import Table, Column
    >>> import numpy as np

### Creating from scratch

A Table can be created without any initial input data or even without any initial columns. This is useful for building tables dynamically if the initial size, columns, or data are not known.

Note

Adding columns or rows requires making a new copy of the entire table table each time, so in the case of large tables this may be slow.

    >>> t = Table()
    >>> t['a'] = [1, 4]
    >>> t['b'] = Column([2.0, 5.0], unit='cm', description='Velocity')
    >>> t['c'] = ['x', 'y']

    >>> t = Table(names=('a', 'b', 'c'), dtype=('f4', 'i4', 'S2'))
    >>> t.add_row((1, 2.0, 'x'))
    >>> t.add_row((4, 5.0, 'y'))

### List input

A typical case is where you have a number of data columns with the same length defined in different variables. These might be Python lists or `numpy` arrays or a mix of the two. These can be used to create a `Table` by putting the column data variables into a Python list. In this case the column names are not defined by the input data, so they must either be set using the `names` keyword or they will be auto-generated as `col<N>`.

    >>> a = [1, 4]
    >>> b = [2.0, 5.0]
    >>> c = ['x', 'y']
    >>> t = Table([a, b, c], names=('a', 'b', 'c'))
    >>> t
    <Table rows=2 names=('a','b','c')>
    array([(1, 2.0, 'x'), (4, 5.0, 'y')],
          dtype=[('a', '<i8'), ('b', '<f8'), ('c', 'S1')])

Make a new table using columns from the first table

Once you have a `Table` then you can make new table by selecting columns and putting this into a Python list, e.g. `[ t['c'], t['a'] ]`:

    >>> Table([t['c'], t['a']])
    <Table rows=2 names=('c','a')>
    array([('x', 1), ('y', 4)],
          dtype=[('c', 'S1'), ('a', '<i8')])

Make a new table using expressions involving columns

The `Column` object is derived from the standard `numpy` array and can be used directly in arithmetic expressions. This allows for a compact way of making a new table with modified column values:

    >>> Table([t['a']**2, t['b'] + 10])
    <Table rows=2 names=('a','b')>
    array([(1, 12.0), (16, 15.0)],
          dtype=[('a', '<i8'), ('b', '<f8')])

Different types of column data

The list input method for `Table` is very flexible since you can use a mix of different data types to initialize a table:

    >>> a = (1, 4)
    >>> b = np.array([[2, 3], [5, 6]])  # vector column
    >>> c = Column(['x', 'y'], name='axis')
    >>> arr = (a, b, c)
    >>> Table(arr)  # Data column named "c" has a name "axis" that table
    <Table rows=2 names=('col0','col1','axis')>
    array([(1, [2, 3], 'x'), (4, [5, 6], 'y')],
          dtype=[('col0', '<i8'), ('col1', '<i8', (2,)), ('axis', 'S1')])

Notice that in the third column the existing column name `'axis'` is used.

Use row data instead of column data

You can also initialize a table with row values. This is constructed as a list of dict objects. The keys determine the column names:

    >>> data = [{'a': 5, 'b': 10}, {'a': 15, 'b': 20}]
    >>> Table(data)
    <Table rows=2 names=('a','b')>
    array([(5, 10), (15, 20)],
          dtype=[('a', '<i8'), ('b', '<i8')])

Every row must have the same set of keys or a ValueError will be thrown:

    >>> t = Table([{'a': 5, 'b': 10}, {'a': 15, 'b': 30, 'c': 50}])
    Traceback (most recent call last):
      ...
    ValueError: Row 0 has no value for column c

### Row input

You can make a new table from a single row of an existing table:

    >>> a = [1, 4]
    >>> b = [2.0, 5.0]
    >>> t = Table([a, b], names=('a', 'b'))
    >>> t2 = Table(t[1])

Remember that a `Row` has effectively a zero length compared to the newly created `Table` which has a length of one. This is similar to the difference between a scalar `1` (length 0) and an array like `np.array([1])` with length 1.

### Dictionary input

A dictionary of column data can be used to initialize a `Table`.

    >>> arr = {'a': [1, 4],
    ...        'b': [2.0, 5.0],
    ...        'c': ['x', 'y']}
    >>>
    >>> Table(arr)
    <Table rows=2 names=('a','c','b')>
    array([(1, 'x', 2.0), (4, 'y', 5.0)],
          dtype=[('a', '<i8'), ('c', 'S1'), ('b', '<f8')])

Specify the column order and optionally the data types

    >>> Table(arr, names=('a', 'b', 'c'), dtype=('f4', 'i4', 'S2'))
    <Table rows=2 names=('a','b','c')>
    array([(1.0, 2, 'x'), (4.0, 5, 'y')],
          dtype=[('a', '<f4'), ('b', '<i4'), ('c', 'S2')])

Different types of column data

The input column data can be any data type that can initialize a `Column` object:

    >>> arr = {'a': (1, 4),
    ...        'b': np.array([[2, 3], [5, 6]]),
    ...        'c': Column(['x', 'y'], name='axis')}
    >>> Table(arr, names=('a', 'b', 'c'))
    <Table rows=2 names=('a','b','c')>
    array([(1, [2, 3], 'x'), (4, [5, 6], 'y')],
          dtype=[('a', '<i8'), ('b', '<i8', (2,)), ('c', 'S1')])

Notice that the key `'c'` takes precendence over the existing column name `'axis'` in the third column. Also see that the `'b'` column is a vector column where each row element is itself a 2-element array.

Renaming columns is not possible

    >>> Table(arr, names=('a_new', 'b_new', 'c_new'))
    Traceback (most recent call last):
      ...
    KeyError: 'a_new'

### NumPy structured array

The structured array is the standard mechanism in `numpy` for storing heterogenous table data. Most scientific I/O packages that read table files (e.g. PyFITS, vo.table, asciitable) will return the table in an object that is based on the structured array. A structured array can be created using:

    >>> arr = np.array([(1, 2.0, 'x'),
    ...                 (4, 5.0, 'y')],
    ...                dtype=[('a', 'i8'), ('b', 'f8'), ('c', 'S2')])

From `arr` it is simple to create the corresponding `Table` object:

    >>> Table(arr)
    <Table rows=2 names=('a','b','c')>
    array([(1, 2.0, 'x'), (4, 5.0, 'y')],
          dtype=[('a', '<i8'), ('b', '<f8'), ('c', 'S2')])

Note that in the above example and most the following ones we are creating a table and immediately asking the interactive Python interpreter to print the table to see what we made. In real code you might do something like:

    >>> table = Table(arr)
    >>> print table
     a   b   c
    --- --- ---
      1 2.0   x
      4 5.0   y

New column names

The column names can be changed from the original values by providing the `names` argument:

    >>> Table(arr, names=('a_new', 'b_new', 'c_new'))
    <Table rows=2 names=('a_new','b_new','c_new')>
    array([(1, 2.0, 'x'), (4, 5.0, 'y')],
          dtype=[('a_new', '<i8'), ('b_new', '<f8'), ('c_new', 'S2')])

New data types

Likewise the data type for each column can by changed with `dtype`:

    >>> Table(arr, dtype=('f4', 'i4', 'S4'))
    <Table rows=2 names=('a','b','c')>
    array([(1.0, 2, 'x'), (4.0, 5, 'y')],
          dtype=[('a', '<f4'), ('b', '<i4'), ('c', 'S4')])

    >>> Table(arr, names=('a_new', 'b_new', 'c_new'), dtype=('f4', 'i4', 'S4'))
    <Table rows=2 names=('a_new','b_new','c_new')>
    array([(1.0, 2, 'x'), (4.0, 5, 'y')],
          dtype=[('a_new', '<f4'), ('b_new', '<i4'), ('c_new', 'S4')])

### NumPy homogeneous array

A normal `numpy` 2-d array (where all elements have the same type) can be converted into a `Table`. In this case the column names are not specified by the data and must either be provided by the user or will be automatically generated as `col<N>` where `<N>` is the column number.

Basic example with automatic column names

    >>> arr = np.array([[1, 2, 3],
    ...                 [4, 5, 6]])
    >>> Table(arr)
    <Table rows=2 names=('col0','col1','col2')>
    array([(1, 2, 3), (4, 5, 6)],
          dtype=[('col0', '<i8'), ('col1', '<i8'), ('col2', '<i8')])

Column names and types specified

    >>> Table(arr, names=('a_new', 'b_new', 'c_new'), dtype=('f4', 'i4', 'S4'))
    <Table rows=2 names=('a_new','b_new','c_new')>
    array([(1.0, 2, '3'), (4.0, 5, '6')],
          dtype=[('a_new', '<f4'), ('b_new', '<i4'), ('c_new', 'S4')])

Referencing the original data

It is possible to reference the original data for an homogeneous array as long as the data types are not changed:

    >>> t = Table(arr, copy=False)

Python arrays versus `numpy` arrays as input

There is a slightly subtle issue that is important to understand in the way that `Table` objects are created. Any data input that looks like a Python list (including a tuple) is considered to be a list of columns. In contrast an homogeneous `numpy` array input is interpreted as a list of rows:

    >>> arr = [[1, 2, 3],
    ...        [4, 5, 6]]
    >>> np_arr = np.array(arr)

    >>> Table(arr)    # Two columns, three rows
    <Table rows=3 names=('col0','col1')>
    array([(1, 4), (2, 5), (3, 6)],
          dtype=[('col0', '<i8'), ('col1', '<i8')])

    >>> Table(np_arr)  # Three columns, two rows
    <Table rows=2 names=('col0','col1','col2')>
    array([(1, 2, 3), (4, 5, 6)],
          dtype=[('col0', '<i8'), ('col1', '<i8'), ('col2', '<i8')])

This dichotomy is needed to support flexible list input while retaining the natural interpretation of 2-d `numpy` arrays where the first index corresponds to data “rows” and the second index corresponds to data “columns”.

If you have a Python list which is structured as a list of data rows, use the following trick to effectively transpose into a list of columns for initializing a `Table` object:

    >>> arr = [[1, 2.0, 'string'],  # list of rows
    ...        [2, 3.0, 'values']]
    >>> col_arr = zip(*arr)  # transpose to a list of columns
    >>> col_arr
    [(1, 2), (2.0, 3.0), ('string', 'values')]
    >>> t = Table(col_arr)

### Table columns

A new table can be created by selecting a subset of columns in an existing table:

    >>> t = Table(names=('a', 'b', 'c'))
    >>> t2 = t['c', 'b', 'a']  # Makes a copy of the data
    >>> print t2
     c   b   a
    --- --- ---

An alternate way to use the `columns` attribute (explained in the TableColumns section) to initialize a new table. This let’s you choose columns by their numerical index or name and supports slicing syntax:

    >>> Table(t.columns[0:2])
    <Table rows=0 names=('a','b')>
    array([],
          dtype=[('a', '<f8'), ('b', '<f8')])

    >>> Table([t.columns[0], t.columns['c']])
    <Table rows=0 names=('a','c')>
    array([],
          dtype=[('a', '<f8'), ('c', '<f8')])

## Initialization Details

A table object is created by initializing a `Table` class object with the following arguments, all of which are optional:

`data` : numpy ndarray, dict, list, or Table
  Data to initialize table.
`names` : list
  Specify column names
`dtype` : list
  Specify column data types
`meta` : dict-like
  Meta-Data associated with the table
`copy` : boolean
  Copy the input data (default=True).

The following subsections provide further detail on the values and options for each of the keyword arguments that can be used to create a new `Table` object.

### data

The `Table` object can be initialized with several different forms for the `data` argument.

numpy ndarray (structured array)
  The base column names are the field names of the `data` structured array. The `names` list (optional) can be used to select particular fields and/or reorder the base names. The `dtype` list (optional) must match the length of `names` and is used to override the existing `data` types.
numpy ndarray (homogeneous)

  The `data` ndarray must be at least 2-dimensional, with the first (left-most) index corresponding to row number (table length) and the second index corresponding to column number (table width). Higher dimensions get absorbed in the shape of each table cell.

  If provided the `names` list must match the “width” of the `data` argument. The default for `names` is to auto-generate column names in the form “col<N>”. If provided the `dtype` list overrides the base column types and must match the length of `names`.

dict-like
  The keys of the `data` object define the base column names. The corresponding values can be Column objects, numpy arrays, or list-like objects. The `names` list (optional) can be used to select particular fields and/or reorder the base names. The `dtype` list (optional) must match the length of `names` and is used to override the existing or default data types.
list-like
  Each item in the `data` list provides a column of data values and can be a Column object, numpy array, or list-like object. The `names` list defines the name of each column. The names will be auto-generated if not provided (either from the `names` argument or by Column objects). If provided the `names` argument must match the number of items in the `data` list. The optional `dtype` list will override the existing or default data types and must match `names` in length.
list-of-dicts
  Similar to Python’s builtin `csv.DictReader`, each item in the `data` list provides a row of data values and must be a dict. The key values in each dict define the column names and each row must have identical column names. The `names` argument may be supplied to specify colum ordering. If it is not provided, the column order will default to alphabetical. The `dtype` list may be specified, and must correspond to the order of output columns. If any row’s keys do no match the rest of the rows, a ValueError will be thrown.
None
  Initialize a zero-length table. If `names` and optionally `dtype` are provided then the corresponding columns are created.

### names

The `names` argument provides a way to specify the table column names or override the existing ones. By default the column names are either taken from existing names (for `ndarray` or `Table` input) or auto-generated as `col<N>`. If `names` is provided then it must be a list with the same length as the number of columns. Any list elements with value `None` fall back to the default name.

In the case where `data` is provided as dict of columns, the `names` argument can be supplied to specify the order of columns. The `names` list must then contain each of the keys in the `data` dict. If `names` is not supplied then the order of columns in the output table is not determinate.

### dtype

The `dtype` argument provides a way to specify the table column data types or override the existing types. By default the types are either taken from existing types (for `ndarray` or `Table` input) or auto-generated by the `numpy.array()` routine. If `dtype` is provided then it must be a list with the same length as the number of columns. The values must be valid `numpy.dtype` initializers or `None`. Any list elements with value `None` fall back to the default type.

In the case where `data` is provided as dict of columns, the `dtype` argument must be accompanied by a corresponding `names` argument in order to uniquely specify the column ordering.

### meta

The `meta` argument is simply an object that contains meta-data associated with the table. It is recommended that this object be a dict or OrderedDict, but the only firm requirement is that it can be copied with the standard library `copy.deepcopy()` routine. By default `meta` is an empty OrderedDict.

### copy

By default the input `data` are copied into a new internal `np.ndarray` object in the Table object. In the case where `data` is either an `np.ndarray` object or an existing `Table`, it is possible to use a reference to the existing data by setting `copy=False`. This has the advantage of reducing memory use and being faster. However one should take care because any modifications to the new Table data will also be seen in the original input data. See the Copy versus Reference section for more information.

## Copy versus Reference

Normally when a new `Table` object is created, the input data are copied into a new internal array object. This ensures that if the new table elements are modified then the original data will not be affected. However, when creating a table from a numpy ndarray object (structured or homogeneous), it is possible to disable copying so that instead a memory reference to the original data is used. This has the advantage of being faster and using less memory. However, caution must be exercised because the new table data and original data will be linked, as shown below:

    >>> arr = np.array([(1, 2.0, 'x'),
    ...                 (4, 5.0, 'y')],
    ...                dtype=[('a', 'i8'), ('b', 'f8'), ('c', 'S2')])
    >>> arr['a']  # column "a" of the input array
    array([1, 4])
    >>> t = Table(arr, copy=False)
    >>> t['a'][1] = 99
    >>> arr['a']  # arr['a'] got changed when we modified t['a']
    array([ 1, 99])

Note that when referencing the data it is not possible to change the data types since that operation requires making a copy of the data. In this case an error occurs:

    >>> t = Table(arr, copy=False, dtype=('f4', 'i4', 'S4'))
    Traceback (most recent call last):
      ...
    ValueError: Cannot specify dtype when copy=False

Another caveat in using referenced data is that you cannot add new row to the table. This generates an error because of conflict between the two references to the same underlying memory. Internally, adding a row may involve moving the data to a new memory location which would corrupt the input data object. `numpy` does not allow this:

    >>> t.add_row([1, 2, 3])
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
      File "astropy/table/table.py", line 760, in add_row
        self._data.resize((newlen,), refcheck=False)
    ValueError: cannot resize this array: it does not own its data

## Column and TableColumns classes

There are two classes, `Column` and `TableColumns`, that are useful when constructing new tables.

### Column

A `Column` object can be created as follows, where in all cases the column `name` should be provided as a keyword argument and one can optionally provide these values:

`data` : list, ndarray or None
  Column data values
`dtype` : numpy.dtype compatible value
  Data type for column
`description` : str
  Full description of column
`unit` : str
  Physical unit
`format` : str or function
  Format specifier for outputting column values
`meta` : dict
  Meta-data associated with the column

#### Initialization options

The column data values, shape, and data type are specified in one of two ways:

Provide a ``data`` value but not a ``length`` or ``shape``

> Examples:

>     col = Column([1, 2], name='a')  # shape=(2,)
>     col = Column([[1, 2], [3, 4]], name='a')  # shape=(2, 2)
>     col = Column([1, 2], name='a', dtype=float)
>     col = Column(np.array([1, 2]), name='a')
>     col = Column(['hello', 'world'], name='a')

> The `dtype` argument can be any value which is an acceptable fixed-size data-type initializer for the numpy.dtype() method. See http://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html. Examples include:

> - Python non-string type (float, int, bool)
> - Numpy non-string type (e.g. np.float32, np.int64, np.bool)
> - Numpy.dtype array-protocol type strings (e.g. ‘i4’, ‘f8’, ‘S15’)

> If no `dtype` value is provide then the type is inferred using `np.array(data)`. When `data` is provided then the `shape` and `length` arguments are ignored.

Provide ``length`` and optionally ``shape``, but not ``data``

> Examples:

>     col = Column(name='a', length=5)
>     col = Column(name='a', dtype=int, length=10, shape=(3,4))

> The default `dtype` is `np.float64`. The `shape` argument is the array shape of a single cell in the column. The default `shape` is () which means a single value in each element.

Note

After setting the type for a column, that type cannot be changed. If data values of a different type are assigned to the column then they will be cast to the existing column type.

#### Format specifier

The format specifier controls the output of column values when a table or column is printed or written to an ASCII table. The format specifier can be either a “old-style” or “new-style” format string or a function:

Old-style

This corresponds to syntax like `"%.4f" % value` as documented in String formatting operations.

> `"%.4f"` to print four digits after the decimal in float format, or

> `"%6d"` to print an integer in a 6-character wide field.

New-style

This corresponds to syntax like `"{:.4f}".format(value)` as documented in format string syntax.

> `"{:.4f}"` to print four digits after the decimal in float format, or

> `"{:6d}"` to print an integer in a 6-character wide field.

Note that in either case any Python format string that formats exactly one value is valid, so `{:.4f} angstroms` or `Value: %12.2f` would both work.

Function

The greatest flexibility can be achieved by setting a formatting function. This function must accept a single argument (the value) and return a string. In the following example this is used to make a LaTeX ready output:

    >>> t = Table([[1,2],[1.234e9,2.34e-12]], names = ('a','b'))
    >>> def latex_exp(value):
    ...     val = '{:8.2}'.format(value)
    ...     mant, exp = val.split('e')
    ...     # remove leading zeros
    ...     exp = exp[0] + exp[1:].lstrip('0')
    ...     return '$ {0} \\times 10^{{ {1} }}$' .format(mant, exp)
    >>> t['b'].format = latex_exp
    >>> t['a'].format = '{0:.4f}'
    >>> import sys
    >>> t.write(sys.stdout, format='latex')
    \begin{table}
    \begin{tabular}{cc}
    a & b \\
    1.0000 & $  1.2 \times 10^{ +9 }$ \\
    2.0000 & $  2.3 \times 10^{ -12 }$ \\
    \end{tabular}
    \end{table}

### TableColumns

Each `Table` object has an attribute `columns` which is an ordered dictionary that stores all of the `Column` objects in the table (see also the Column section). Technically the `columns` attribute is a `TableColumns` object, which is an enhanced ordered dictionary that provides easier ways to select multiple columns. There are a few key points to remember:

- A `Table` can be initialized from a `TableColumns` object (copy is always True).
- Selecting multiple columns from a `TableColumns` object returns another `TableColumns` object.
- Select one column from a `TableColumns` object returns a `Column`.

So now look at the ways to select columns from a `TableColumns` object:

Select columns by name

    >>> t = Table(names=('a', 'b', 'c', 'd'))

    >>> t.columns['d', 'c', 'b']
    <TableColumns names=('d','c','b')>

Select columns by index slicing

    >>> t.columns[0:2]  # Select first two columns
    <TableColumns names=('a','b')>

    >>> t.columns[::-1]  # Reverse column order
    <TableColumns names=('d','c','b','a')>

Select column by index or name

    >>> t.columns[1]  # Choose columns by index
    <Column name='b' unit=None format=None description=None>
    array([], dtype=float64)

    >>> t.columns['b']  # Choose column by name
    <Column name='b' unit=None format=None description=None>
    array([], dtype=float64)