Agroalimentary (2 points)
=========================

From the Catalan Government web we have downloaded the file
:download:`agroalimentary.csv` related to Catalan agroalimentary
companies, that you already know. We have extended it with a randomly
generated column named ``Facturacio`` showing the company billing in
thousands of euros (:py:class:`int`). Download and study this file. We
have already created a :py:ref:`DataFrame <basics.dataframe>` with
this data.

The following example shows the names of all the columns of this
:py:ref:`DataFrame <basics.dataframe>`, then a subdataframe example
with columns corresponding to the company name (``Establiment``), the
region (``Comarca``), the company type (``Titularitat``) and the
billing (``Facturacio``), and, below, the same subdataframe with
columns ``Establiment`` and ``Sector``:

.. literalinclude:: test-agro.txt
   :language: python
   :lines: 4-31
	  
Save the two following functions to file ``agro.py``.

#. Write function :py:func:`agro1` that takes a :py:ref:`DataFrame
   <basics.dataframe>` as that described, a sector (:py:class:`str`)
   and a company type (:py:class:`str`). This function returns a tuple
   with a :py:ref:`DataFrame <basics.dataframe>` and an integer
   (:py:class:`int`). The returned :py:ref:`DataFrame
   <basics.dataframe>` is a subdataframe of the given one, with only
   those rows that correspond to the given sector and company type and
   the two columns ``'Establiment'`` and ``'Facturacio'``. This
   subdataframe is sorted by the column ``'Facturacio'`` in descending
   order. The returned integer is the total billing (column
   ``'Facturacio'``) of the computed subdataframe (1 point). Examples:
   
   .. literalinclude:: test-agro1.txt
      :language: python
      :lines: 6 - 21

   .. note:: You have more tests in file :download:`test-agro1.txt`
	   
   .. hint:: Use selection, methods ``sort_values`` and ``sum``.
   
#. Write function :py:func:`agro2` that takes a :py:ref:`DataFrame
   <basics.dataframe>`, as that described and two latitude
   values, :math:`la1` and :math:`la2` (:py:class:`float`), such that
   :math:`la1<la2`. This function considers the subset of companies
   located at a latitude :math:`la` such that :math:`la1 \leq la \leq
   la2` and returns a :py:ref:`Series <basics.Series>` in
   which the index is the sector (:py:class:`str`) and
   the value is the billing mean of the companies of this sector
   (:py:class:`float`), for the given latitudes inteval (1 point). Examples:
   
   .. literalinclude:: test-agro2.txt
      :language: python
      :lines: 6-16

   .. note:: You have more tests in file :download:`test-agro2.txt`

   .. hint:: Use selection and methods ``groupby`` and ``mean``.


.. note:: To run your functions you don't actually need to read the given data file. But you need to download and save it into your working folder because the given testfiles read from it to create the :py:ref:`DataFrame <basics.dataframe>`.