.. _seance5cubemultidimensionnelcorrectionrst:

===================================
Cube multidimensionnel - correction
===================================


.. only:: html

    **Links:** :download:`notebook <seance5_cube_multidimensionnel_correction.ipynb>`, :downloadlink:`html <seance5_cube_multidimensionnel_correction2html.html>`, :download:`PDF <seance5_cube_multidimensionnel_correction.pdf>`, :download:`python <seance5_cube_multidimensionnel_correction.py>`, :downloadlink:`slides <seance5_cube_multidimensionnel_correction.slides.html>`, :githublink:`GitHub|_doc/notebooks/sessions/seance5_cube_multidimensionnel_correction.ipynb|*`


Manipulation de tables de mortalités façon OLAP, correction des
exercices.

.. code:: ipython3

    %matplotlib inline
    import matplotlib.pyplot as plt
    plt.style.use('ggplot')
    import pyensae
    from pyquickhelper.helpgen import NbImage
    from jyquickhelper import add_notebook_menu
    add_notebook_menu()


.. parsed-literal::
    Populating the interactive namespace from numpy and matplotlib






.. contents::
    :local:





On lit les données puis on recrée un
`DataSet <http://xarray.pydata.org/en/stable/data-structures.html#dataset>`__
:

.. code:: ipython3

    from actuariat_python.data import table_mortalite_euro_stat 
    table_mortalite_euro_stat()
    import pandas
    df = pandas.read_csv("mortalite.txt", sep="\t", encoding="utf8", low_memory=False)
    df2 = df[["annee", "age_num","indicateur","pays","genre","valeur"]].dropna().reset_index(drop=True)
    piv = df2.pivot_table(index=["annee", "age_num","pays","genre"],
                    columns=["indicateur"],
                   values="valeur")
    import xarray
    ds = xarray.Dataset.from_dataframe(piv)
    ds




.. parsed-literal::
    <xarray.Dataset>
    Dimensions:     (age_num: 84, annee: 54, genre: 3, pays: 54)
    Coordinates:
      * annee       (annee) int64 1960 1961 1962 1963 1964 1965 1966 1967 1968 ...
      * age_num     (age_num) float64 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 ...
      * pays        (pays) object 'AM' 'AT' 'AZ' 'BE' 'BG' 'BY' 'CH' 'CY' 'CZ' ...
      * genre       (genre) object 'F' 'M' 'T'
    Data variables:
        DEATHRATE   (annee, age_num, pays, genre) float64 nan nan nan nan nan ...
        LIFEXP      (annee, age_num, pays, genre) float64 nan nan nan nan nan ...
        PROBDEATH   (annee, age_num, pays, genre) float64 nan nan nan nan nan ...
        PROBSURV    (annee, age_num, pays, genre) float64 nan nan nan nan nan ...
        PYLIVED     (annee, age_num, pays, genre) float64 nan nan nan nan nan ...
        SURVIVORS   (annee, age_num, pays, genre) float64 nan nan nan nan nan ...
        TOTPYLIVED  (annee, age_num, pays, genre) float64 nan nan nan nan nan ...



Exercice 1 : que font les lignes suivantes ?
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Le programme suivant uilise les fonctions `align nad
reindex <http://xarray.pydata.org/en/stable/data-structures.html?highlight=assign#transforming-datasets>`__
pour faire une moyenne sur une des dimensions du DataSet (le pays) puis
à ajouter une variable *meanp* contenant le résultat.

.. code:: ipython3

    ds.assign(LIFEEXP_add = ds.LIFEXP-1)




.. parsed-literal::
    <xarray.Dataset>
    Dimensions:      (age_num: 84, annee: 54, genre: 3, pays: 54)
    Coordinates:
      * annee        (annee) int64 1960 1961 1962 1963 1964 1965 1966 1967 1968 ...
      * age_num      (age_num) float64 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 ...
      * pays         (pays) object 'AM' 'AT' 'AZ' 'BE' 'BG' 'BY' 'CH' 'CY' 'CZ' ...
      * genre        (genre) object 'F' 'M' 'T'
    Data variables:
        DEATHRATE    (annee, age_num, pays, genre) float64 nan nan nan nan nan ...
        LIFEXP       (annee, age_num, pays, genre) float64 nan nan nan nan nan ...
        PROBDEATH    (annee, age_num, pays, genre) float64 nan nan nan nan nan ...
        PROBSURV     (annee, age_num, pays, genre) float64 nan nan nan nan nan ...
        PYLIVED      (annee, age_num, pays, genre) float64 nan nan nan nan nan ...
        SURVIVORS    (annee, age_num, pays, genre) float64 nan nan nan nan nan ...
        TOTPYLIVED   (annee, age_num, pays, genre) float64 nan nan nan nan nan ...
        LIFEEXP_add  (annee, age_num, pays, genre) float64 nan nan nan nan nan ...



.. code:: ipython3

    meanp = ds.mean(dim="pays")
    ds1, ds2 = xarray.align(ds, meanp, join='outer')

.. code:: ipython3

    joined = ds1.assign(meanp = ds2["LIFEXP"])

.. code:: ipython3

    joined.to_dataframe().head()






.. raw:: html

    <div>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th>DEATHRATE</th>
          <th>LIFEXP</th>
          <th>PROBDEATH</th>
          <th>PROBSURV</th>
          <th>PYLIVED</th>
          <th>SURVIVORS</th>
          <th>TOTPYLIVED</th>
          <th>meanp</th>
        </tr>
        <tr>
          <th>age_num</th>
          <th>annee</th>
          <th>genre</th>
          <th>pays</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th rowspan="5" valign="top">1</th>
          <th rowspan="5" valign="top">1960</th>
          <th rowspan="5" valign="top">F</th>
          <th>AM</th>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>73.52</td>
        </tr>
        <tr>
          <th>AT</th>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>73.52</td>
        </tr>
        <tr>
          <th>AZ</th>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>73.52</td>
        </tr>
        <tr>
          <th>BE</th>
          <td>0.00159</td>
          <td>73.7</td>
          <td>0.00159</td>
          <td>0.99841</td>
          <td>97316</td>
          <td>97393</td>
          <td>7179465</td>
          <td>73.52</td>
        </tr>
        <tr>
          <th>BG</th>
          <td>0.00652</td>
          <td>73.2</td>
          <td>0.00650</td>
          <td>0.99350</td>
          <td>95502</td>
          <td>95813</td>
          <td>7017023</td>
          <td>73.52</td>
        </tr>
      </tbody>
    </table>
    </div>



Les valeurs *meanp* sont constantes quelque soient le pays à *annee*,
*age_num*, *genre* fixés.

.. code:: ipython3

    joined.sel(annee=2000, age_num=59, genre='F')["meanp"]




.. parsed-literal::
    <xarray.DataArray 'meanp' ()>
    array(23.83243243243243)
    Coordinates:
        annee    int64 2000
        genre    object 'F'
        age_num  float64 59.0