# `pandas`_ is a popular data analysis tool, especially for working with tabular data.
# You can convert your :py:class:`~hdmf.common.table.DynamicTable` to a
# :py:class:`~pandas.DataFrame` using
# :py:meth:`DynamicTable.to_dataframe <hdmf.common.table.DynamicTable.to_dataframe>`.
# Accessing the table as a :py:class:`~pandas.DataFrame` provides you with powerful,
# standard methods for indexing, selecting, and querying tabular data from `pandas`_.
# This is the recommended method of reading data from your table. See also the `pandas indexing documentation`_.
# Printing a :py:class:`~hdmf.common.table.DynamicTable` as a :py:class:`~pandas.DataFrame`
# or displaying the :py:class:`~pandas.DataFrame` in Jupyter shows a more intuitive
# tabular representation of the data than printing the
# :py:class:`~hdmf.common.table.DynamicTable` object.
#
# .. _pandas: https://pandas.pydata.org/
# .. _`pandas indexing documentation`: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html
df = table.to_dataframe()
###############################################################################
# .. note::
#
# Changes to the ``DataFrame`` will not be saved in the ``DynamicTable``.
###############################################################################
# Converting the table from a pandas ``DataFrame``
# ------------------------------------------------
# If your data is already in a :py:class:`~pandas.DataFrame`, you can convert the
# ``DataFrame`` to a :py:class:`~hdmf.common.table.DynamicTable` using the class method
# :py:meth:`DynamicTable.from_dataframe <hdmf.common.table.DynamicTable.from_dataframe>`.
table_from_df = DynamicTable.from_dataframe(
name='my_table',
# `pandas`_ is a popular data analysis tool, especially for working with tabular data.
# You can convert your :py:class:`~hdmf.common.table.DynamicTable` to a
# :py:class:`~pandas.DataFrame` using
# :py:meth:`DynamicTable.to_dataframe <hdmf.common.table.DynamicTable.to_dataframe>`.
# Accessing the table as a :py:class:`~pandas.DataFrame` provides you with powerful,
# standard methods for indexing, selecting, and querying tabular data from `pandas`_,
# and is recommended. See also the `pandas indexing documentation`_.
# Printing a :py:class:`~hdmf.common.table.DynamicTable` as a :py:class:`~pandas.DataFrame`
# or displaying the :py:class:`~pandas.DataFrame` in Jupyter shows a more intuitive
# tabular representation of the data than printing the
# :py:class:`~hdmf.common.table.DynamicTable` object.
#
# .. _pandas: https://pandas.pydata.org/
# .. _`pandas indexing documentation`: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html
df = table.to_dataframe()
###############################################################################
# .. note::
#
# Changes to the ``DataFrame`` will not be saved in the ``DynamicTable``.
###############################################################################
# Converting the table from a pandas ``DataFrame``
# ------------------------------------------------
# If your data is already in a :py:class:`~pandas.DataFrame`, you can convert the
# ``DataFrame`` to a :py:class:`~hdmf.common.table.DynamicTable` using the class method
# :py:meth:`DynamicTable.from_dataframe <hdmf.common.table.DynamicTable.from_dataframe>`.
table_from_df = DynamicTable.from_dataframe(
name='my table',