.. _td2aecoexercicesdemanipulationdedonneescorrectionbrst:

==================================================================================================
2A.eco - Mise en pratique des séances 1 et 2 - Utilisation de pandas et visualisation - correction
==================================================================================================


.. only:: html

    **Links:** :download:`notebook <td2a_eco_exercices_de_manipulation_de_donnees_correction_b.ipynb>`, :downloadlink:`html <td2a_eco_exercices_de_manipulation_de_donnees_correction_b2html.html>`, :download:`python <td2a_eco_exercices_de_manipulation_de_donnees_correction_b.py>`, :downloadlink:`slides <td2a_eco_exercices_de_manipulation_de_donnees_correction_b.slides.html>`, :githublink:`GitHub|_doc/notebooks/td2a_eco/td2a_eco_exercices_de_manipulation_de_donnees_correction_b.ipynb|*`


Correction de l’exercice 2 et manipulations classiques de texte.

.. code:: ipython3

    %matplotlib inline

.. code:: ipython3

    from jyquickhelper import add_notebook_menu
    add_notebook_menu()






.. contents::
    :local:





.. code:: ipython3

    from pyensae.datasource import download_data
    files = download_data("td2a_eco_exercices_de_manipulation_de_donnees.zip",
                          url="https://github.com/sdpython/ensae_teaching_cs/raw/master/_doc/notebooks/td2a_eco/data/")
    files




.. parsed-literal::
    ['Players_WC2014.xlsx', '.\\velib_t1.txt', '.\\velib_t2.txt', '.\\villes.txt']



Exercice 2 - Les villes
-----------------------

Durée : 40 minutes

1. Importer la base des villes villes.xls
2. Les noms de variables et les observations contiennent des espaces
   inutiles (exemple : ‘MAJ’) : commnecer par nettoyer l’ensemble des
   chaines de caractères (à la fois dans les noms de colonnes et dans
   les observations)
3. Trouver le nombre de codes INSEE différents (attention aux doublons)
4. Comment calculer rapidement la moyenne, le nombre et le maximum pour
   chaque variable numérique ? (une ligne de code)
5. Compter le nombre de villes dans chaque Region et en faire un
   dictionnaire où la clé est la région et la valeur le nombre de villes
6. Représenter les communes en utilisant

   -  matplotlib
   -  une librairie de cartographie (ex : folium)

.. code:: ipython3

    import pandas
    df_villes = pandas.read_csv("villes.txt", sep="\t", encoding="utf-8")
    print(df_villes.columns)


.. parsed-literal::
    Index([' Nom Ville ', 'MAJ   ', ' Code Postal ', ' Code INSEE ', 'Code Région',
           ' Latitude ', ' Longitude ', ' Eloignement '],
          dtype='object')


.. code:: ipython3

    df_villes.head()






.. raw:: html

    <div>
    <style>
        .dataframe thead tr:only-child th {
            text-align: right;
        }

        .dataframe thead th {
            text-align: left;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>Nom Ville</th>
          <th>MAJ</th>
          <th>Code Postal</th>
          <th>Code INSEE</th>
          <th>Code Région</th>
          <th>Latitude</th>
          <th>Longitude</th>
          <th>Eloignement</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>L'Abergement-Clémenciat</td>
          <td>L ABERGEMENT CLEMENCIAT</td>
          <td>1400</td>
          <td>1001</td>
          <td>82</td>
          <td>46.150000</td>
          <td>4.916667</td>
          <td>1.46</td>
        </tr>
        <tr>
          <th>1</th>
          <td>L'Abergement-de-Varey</td>
          <td>L ABERGEMENT DE VAREY</td>
          <td>1640</td>
          <td>1002</td>
          <td>82</td>
          <td>46.000000</td>
          <td>5.416667</td>
          <td>1</td>
        </tr>
        <tr>
          <th>2</th>
          <td>Amareins</td>
          <td>AMAREINS</td>
          <td>1090</td>
          <td>1003</td>
          <td>82</td>
          <td>46.083333</td>
          <td>4.8</td>
          <td>1.85</td>
        </tr>
        <tr>
          <th>3</th>
          <td>Ambérieu-en-Bugey</td>
          <td>AMBERIEU EN BUGEY</td>
          <td>1500</td>
          <td>1004</td>
          <td>82</td>
          <td>45.950000</td>
          <td>5.35</td>
          <td>0.8</td>
        </tr>
        <tr>
          <th>4</th>
          <td>Ambérieux-en-Dombes</td>
          <td>AMBERIEUX EN DOMBES</td>
          <td>1330</td>
          <td>1005</td>
          <td>82</td>
          <td>46.000000</td>
          <td>4.9</td>
          <td>1.72</td>
        </tr>
      </tbody>
    </table>
    </div>



Nettoyage des noms
------------------

.. code:: ipython3

    # Corriger les espaces en trop
    # solution pour les espaces
    # les colonnes
    df_villes.rename(columns=lambda x: x.strip(),inplace = True)
    # les observations
    df_villes['Nom Ville'] = df_villes[['Nom Ville']].applymap(lambda x: x.strip())
    df_villes['MAJ'] = df_villes[['MAJ']].applymap(lambda x: x.strip())

.. code:: ipython3

    df_villes.columns




.. parsed-literal::
    Index(['Nom Ville', 'MAJ', 'Code Postal', 'Code INSEE', 'Code Région',
           'Latitude', 'Longitude', 'Eloignement'],
          dtype='object')



Nombre de codes commune
-----------------------

.. code:: ipython3

    len(df_villes['Code INSEE'].unique())




.. parsed-literal::
    35248



Stats desc
----------

.. code:: ipython3

    df_villes.describe()






.. raw:: html

    <div>
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        .dataframe thead tr:only-child th {
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        .dataframe thead th {
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            vertical-align: top;
        }
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    <table border="1" class="dataframe">
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        <tr style="text-align: right;">
          <th></th>
          <th>Code Postal</th>
          <th>Code INSEE</th>
          <th>Code Région</th>
          <th>Latitude</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>count</th>
          <td>35249.000000</td>
          <td>35249.000000</td>
          <td>35249.000000</td>
          <td>35249.000000</td>
        </tr>
        <tr>
          <th>mean</th>
          <td>46291.014554</td>
          <td>46186.992822</td>
          <td>48.532497</td>
          <td>46.998450</td>
        </tr>
        <tr>
          <th>std</th>
          <td>25418.598824</td>
          <td>25411.240496</td>
          <td>27.264257</td>
          <td>2.206877</td>
        </tr>
        <tr>
          <th>min</th>
          <td>1000.000000</td>
          <td>1001.000000</td>
          <td>0.000000</td>
          <td>41.391667</td>
        </tr>
        <tr>
          <th>25%</th>
          <td>24640.000000</td>
          <td>24550.000000</td>
          <td>24.000000</td>
          <td>45.216667</td>
        </tr>
        <tr>
          <th>50%</th>
          <td>49150.000000</td>
          <td>49074.000000</td>
          <td>43.000000</td>
          <td>47.450000</td>
        </tr>
        <tr>
          <th>75%</th>
          <td>66720.000000</td>
          <td>66203.000000</td>
          <td>72.000000</td>
          <td>48.850000</td>
        </tr>
        <tr>
          <th>max</th>
          <td>95880.000000</td>
          <td>95690.000000</td>
          <td>94.000000</td>
          <td>51.083333</td>
        </tr>
      </tbody>
    </table>
    </div>



Régions et nombre de communes
-----------------------------

.. code:: ipython3

    df_villes.groupby(['Code Région']).size().to_dict()




.. parsed-literal::
    {0: 86,
     11: 4181,
     21: 1462,
     22: 1433,
     23: 1342,
     24: 1766,
     25: 1774,
     26: 1483,
     31: 1494,
     41: 1825,
     43: 1104,
     52: 1188,
     53: 1243,
     54: 830,
     72: 5450,
     73: 1535,
     74: 837,
     82: 2368,
     83: 977,
     91: 1871,
     93: 662,
     94: 338}



Représenter visuellement les communes
-------------------------------------

La conversion depuis Excel (un copier coller) a laissé quelques cellules
négative et presque nulles ``-``. On les remplace par des valeurs
nulles.

.. code:: ipython3

    import pandas
    df_villes.Longitude = df_villes.Longitude.apply(lambda x: "0" if x.strip() == "-" else x)
    df_villes.Longitude = pandas.to_numeric(df_villes.Longitude)

matplotlib
~~~~~~~~~~

.. code:: ipython3

    import matplotlib.pyplot as plt
    plt.figure(figsize=(10,10))
    plt.scatter(df_villes.Longitude, df_villes.Latitude, s=0.1)




.. parsed-literal::
    <matplotlib.collections.PathCollection at 0x218487891d0>




.. image:: td2a_eco_exercices_de_manipulation_de_donnees_correction_b_19_1.png


Exemple avec Folium
~~~~~~~~~~~~~~~~~~~

.. code:: ipython3

    import folium
    import random
    
    locations = df_villes[['Latitude', 'Longitude']].copy()
    print(locations.shape, locations.dropna().shape)
    locations.dropna(inplace = True)
    
    locationlist = locations.values.tolist()
    len(locationlist)
    locationlist[7]


.. parsed-literal::
    (35249, 2) (35249, 2)




.. parsed-literal::

    [45.933333000000005, 5.3333330000000005]



.. code:: ipython3

    communes_random = random.sample(locationlist, 50)

.. code:: ipython3

    map = folium.Map(location=[47.088615, 2.637424], zoom_start=6)
    for point in range(0,len(communes_random)) :
        folium.Marker(communes_random[point]).add_to(map)
    map






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