.. currentmodule:: pandas

.. ipython:: python
   :suppress:

   import numpy as np
   import pandas as pd
   np.set_printoptions(precision=4, suppress=True)
   pd.options.display.max_rows = 8
   index = pd.date_range('1/1/2000', periods=8)
   df = pd.DataFrame(np.random.randn(8, 3), index=index,
                     columns=['A', 'B', 'C'])
   s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])
   wp = pd.Panel(np.random.randn(2, 5, 4), items=['Item1', 'Item2'],
                 major_axis=pd.date_range('1/1/2000', periods=5),
                 minor_axis=['A', 'B', 'C', 'D'])
   
    

.. _basics.attrs:

Attributes and the raw ndarray(s)
---------------------------------

pandas objects have a number of attributes enabling you to access the metadata

  * **shape**: gives the axis dimensions of the object, consistent with ndarray
  * Axis labels

    * **Series**: *index* (only axis)
    * **DataFrame**: *index* (rows) and *columns*
    * **Panel**: *items*, *major_axis*, and *minor_axis*



Note, **these attributes can be safely assigned to**!

.. ipython:: python

   df
   df[:2]
   df.columns = [x.lower() for x in df.columns]
   df

To get the actual data inside a data structure, one need only access the
**values** property:

.. ipython:: python

    s
    s.values

    df
    df.values

    wp
    wp.values

If a DataFrame or Panel contains homogeneously-typed data, the ndarray can
actually be modified in-place, and the changes will be reflected in the data
structure. For heterogeneous data (e.g. some of the DataFrame's columns are not
all the same dtype), this will not be the case. The values attribute itself,
unlike the axis labels, cannot be assigned to.

.. note::

    When working with heterogeneous data, the dtype of the resulting ndarray
    will be chosen to accommodate all of the data involved. For example, if
    strings are involved, the result will be of object dtype. If there are only
    floats and integers, the resulting array will be of float dtype.