.. code:: 

    from functools import partial
    from rpy2.ipython import html
    html.html_rdataframe=partial(html.html_rdataframe, table_class="docutils")

``R`` and ``pandas`` data frames
================================

R ``data.frame`` and :class:``pandas.DataFrame`` objects share a lot of
conceptual similarities, and :mod:``pandas`` chose to use the class name
``DataFrame`` after R objects.

In a nutshell, both are sequences of vectors (or arrays) of consistent
length or size for the first dimension (the "number of rows"). if coming
from the database world, an other way to look at them is column-oriented
data tables, or data table API.

rpy2 is providing an interface between Python and R, and a convenience
conversion layer between :class:``rpy2.robjects.vectors.DataFrame`` and
:class:``pandas.DataFrame`` objects, implemented in
:mod:``rpy2.robjects.pandas2ri``.

.. code:: 

    import pandas as pd
    import rpy2.robjects as ro
    from rpy2.robjects.packages import importr 
    from rpy2.robjects import pandas2ri
    
    from rpy2.robjects.conversion import localconverter

From ``pandas`` to ``R``
------------------------

Pandas data frame:

.. code:: 

    pd_df = pd.DataFrame({'int_values': [1,2,3],
                          'str_values': ['abc', 'def', 'ghi']})
    
    pd_df




.. raw:: html

    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }
    
        .dataframe tbody tr th {
            vertical-align: top;
        }
    
        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>int_values</th>
          <th>str_values</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>1</td>
          <td>abc</td>
        </tr>
        <tr>
          <th>1</th>
          <td>2</td>
          <td>def</td>
        </tr>
        <tr>
          <th>2</th>
          <td>3</td>
          <td>ghi</td>
        </tr>
      </tbody>
    </table>
    </div>



R data frame converted from a ``pandas`` data frame:

.. code:: 

    with localconverter(ro.default_converter + pandas2ri.converter):
      r_from_pd_df = ro.conversion.py2rpy(pd_df)
    
    r_from_pd_df




.. raw:: html

    
            <span>R/rpy2 DataFrame (3 x 2)</span>
            <table>
              <thead>
                <tr>
                  
                  <th>int_values</th>
                  
                  <th>str_values</th>
                  
                </tr>
              </thead>
              <tbody>
              
              <tr>
                
                <td>
                  ...
                </td>
                
                <td>
                  ...
                </td>
                
              </tr>
              
              </tbody>
            </table>
        



The conversion is automatically happening when calling R functions. For
example, when calling the R function ``base::summary``:

.. code:: 

    base = importr('base')
    
    with localconverter(ro.default_converter + pandas2ri.converter):
      df_summary = base.summary(pd_df)
    print(df_summary)


.. parsed-literal::

    ['Min.   :1.0  ' '1st Qu.:1.5  ' 'Median :2.0  ' 'Mean   :2.0  '
     '3rd Qu.:2.5  ' 'Max.   :3.0  ' 'Length:3          ' 'Class :character  '
     'Mode  :character  ' 'NA' 'NA' 'NA']


Note that a ``ContextManager`` is used to limit the scope of the
conversion. Without it, rpy2 will not know how to convert a pandas data
frame:

.. code:: 

    try:
      df_summary = base.summary(pd_df)
    except NotImplementedError as nie:
      print('NotImplementedError:')
      print(nie)


.. parsed-literal::

    NotImplementedError:
    Conversion 'py2rpy' not defined for objects of type '<class 'pandas.core.frame.DataFrame'>'


From ``R`` to ``pandas``
------------------------

Starting from an R data frame this time:

.. code:: 

    r_df = ro.DataFrame({'int_values': ro.IntVector([1,2,3]),
                         'str_values': ro.StrVector(['abc', 'def', 'ghi'])})
    
    r_df




.. raw:: html

    
            <span>R/rpy2 DataFrame (3 x 2)</span>
            <table>
              <thead>
                <tr>
                  
                  <th>int_values</th>
                  
                  <th>str_values</th>
                  
                </tr>
              </thead>
              <tbody>
              
              <tr>
                
                <td>
                  ...
                </td>
                
                <td>
                  ...
                </td>
                
              </tr>
              
              </tbody>
            </table>
        



It can be converted to a pandas data frame using the same converter:

.. code:: 

    with localconverter(ro.default_converter + pandas2ri.converter):
      pd_from_r_df = ro.conversion.rpy2py(r_df)
    
    pd_from_r_df




.. raw:: html

    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }
    
        .dataframe tbody tr th {
            vertical-align: top;
        }
    
        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>int_values</th>
          <th>str_values</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>1</th>
          <td>1</td>
          <td>abc</td>
        </tr>
        <tr>
          <th>2</th>
          <td>2</td>
          <td>def</td>
        </tr>
        <tr>
          <th>3</th>
          <td>3</td>
          <td>ghi</td>
        </tr>
      </tbody>
    </table>
    </div>



Date and time objects
---------------------

.. code:: 

    pd_df = pd.DataFrame({
        'Timestamp': pd.date_range('2017-01-01 00:00:00', periods=10, freq='s')
        })
        
    pd_df




.. raw:: html

    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }
    
        .dataframe tbody tr th {
            vertical-align: top;
        }
    
        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>Timestamp</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>2017-01-01 00:00:00</td>
        </tr>
        <tr>
          <th>1</th>
          <td>2017-01-01 00:00:01</td>
        </tr>
        <tr>
          <th>2</th>
          <td>2017-01-01 00:00:02</td>
        </tr>
        <tr>
          <th>3</th>
          <td>2017-01-01 00:00:03</td>
        </tr>
        <tr>
          <th>4</th>
          <td>2017-01-01 00:00:04</td>
        </tr>
        <tr>
          <th>5</th>
          <td>2017-01-01 00:00:05</td>
        </tr>
        <tr>
          <th>6</th>
          <td>2017-01-01 00:00:06</td>
        </tr>
        <tr>
          <th>7</th>
          <td>2017-01-01 00:00:07</td>
        </tr>
        <tr>
          <th>8</th>
          <td>2017-01-01 00:00:08</td>
        </tr>
        <tr>
          <th>9</th>
          <td>2017-01-01 00:00:09</td>
        </tr>
      </tbody>
    </table>
    </div>



.. code:: 

    with localconverter(ro.default_converter + pandas2ri.converter):
      r_from_pd_df = ro.conversion.py2rpy(pd_df)
    
    r_from_pd_df




.. raw:: html

    
            <span>R/rpy2 DataFrame (10 x 1)</span>
            <table>
              <thead>
                <tr>
                  
                  <th>Timestamp</th>
                  
                </tr>
              </thead>
              <tbody>
              
              <tr>
                
                <td>
                  ...
                </td>
                
              </tr>
              
              </tbody>
            </table>
        



The timezone used for conversion is the system's default timezone unless
``pandas2ri.default_timezone`` is specified... or unless the time zone
is specified in the original time object:

.. code:: 

    pd_tz_df = pd.DataFrame({
        'Timestamp': pd.date_range('2017-01-01 00:00:00', periods=10, freq='s',
                                   tz='UTC')
        })
        
    with localconverter(ro.default_converter + pandas2ri.converter):
      r_from_pd_tz_df = ro.conversion.py2rpy(pd_tz_df)
    
    r_from_pd_tz_df




.. raw:: html

    
            <span>R/rpy2 DataFrame (10 x 1)</span>
            <table>
              <thead>
                <tr>
                  
                  <th>Timestamp</th>
                  
                </tr>
              </thead>
              <tbody>
              
              <tr>
                
                <td>
                  ...
                </td>
                
              </tr>
              
              </tbody>
            </table>