Note

This is an old version of the documentation. See http://docs.astropy.org/en/stable for the latest version.

# Interfacing with the pandas packageΒΆ

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 be able to easily 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()`

.

To demonstrate these, we can create a simple 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 pandas 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/from pandas are subject to the following caveats:

- The pandas DataFrame structure does not support multi-dimensional
columns, so
`Table`

objects with multi-dimensional columns cannot be converted to DataFrame. - Masked tables can be converted, but DataFrame uses
`numpy.nan`

to indicate masked values, so all numerical columns (integer or float) are converted to`numpy.float`

columns in DataFrame, and string columns with missing values are converted to object columns with`numpy.nan`

values to indicate missing values. For numerical 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, and the different for example integer columns will be converted to floating-point. - Tables with mixin columns can currently not be converted, but this may be implemented in the future.