# Interfacing with the Pandas Package#

The pandas package is a package for high
performance data analysis of table-like structures that is complementary to the
`Table`

class in `astropy`

.

In order to exchange data between the `Table`

class and
the `pandas.DataFrame`

class (the main data structure in `pandas`

),
the `Table`

class includes two methods, `to_pandas()`

and `from_pandas()`

.

## Example#

To demonstrate, we can create a minimal table:

```
>>> from astropy.table import Table
>>> t = Table()
>>> t['a'] = [1, 2, 3, 4]
>>> t['b'] = ['a', 'b', 'c', 'd']
```

Which we can then convert to a `DataFrame`

:

```
>>> df = t.to_pandas()
>>> df
a b
0 1 a
1 2 b
2 3 c
3 4 d
>>> type(df)
<class 'pandas.core.frame.DataFrame'>
```

It is also possible to create a table from a `DataFrame`

:

```
>>> t2 = Table.from_pandas(df)
>>> t2
<Table length=4>
a b
int64 string8
----- -------
1 a
2 b
3 c
4 d
```

The conversions to and from `pandas`

are subject to the following caveats:

The

`DataFrame`

structure does not support multidimensional columns, so`Table`

objects with multidimensional columns cannot be converted to`DataFrame`

.Masked tables can be converted, but in columns of

`float`

or string values the resulting`DataFrame`

uses`numpy.nan`

to indicate missing values. For`float`

columns, the conversion therefore does not necessarily round-trip if converting back to an`astropy`

table, because the distinction between`numpy.nan`

and masked values is lost. This is not a problem for integer columns.Tables with Mixin Columns can not be converted.