.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_covid_r0.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_plot_covid_r0.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_plot_covid_r0.py:


Représentation du R0 par départements
=====================================

Cet exemple reprend des données de tests par gouvernement
pour calculer le coefficient R0 de l'épidémie. La méthode
de Cori est utilisée avec la formule proposée dans :epkg:`covidtracker` :

.. math::

    R = \frac{\sum_{i=t-6}^{t}C_i}{\sum_{i=t-13}^{t-7}C_i}

Où :math:`C_i` est le nombre de cas positifs du jour *i*.
Cette méthode est implémentée dans le package R
`EpiEstim <https://github.com/mrc-ide/EpiEstim>`_ ou
:epkg:`epyestim` pour le langage python. Dans cet exemple,
il sera calculé directement à partir des données.

Sources de données:

* `Données relatives aux résultats des tests virologiques COVID-19 SI-DEP
  <https://www.data.gouv.fr/fr/datasets/
  donnees-relatives-aux-resultats-des-tests-virologiques-covid-19/>`_
* `Contours géographiques des départements
  <https://www.data.gouv.fr/en/datasets/
  contours-geographiques-des-departements/>`_

.. contents::
    :local:

Récupération des données
++++++++++++++++++++++++

.. GENERATED FROM PYTHON SOURCE LINES 35-58

.. code-block:: default


    import warnings
    from pandas import Timedelta, DataFrame
    from geopandas import GeoDataFrame
    import matplotlib.pyplot as plt
    from mpl_toolkits.axes_grid1 import make_axes_locatable
    from aftercovid.data import (
        data_covid_france_departments_tests,
        data_france_departments)


    def valid(df):
        "Checks that a dataframe is not empty."
        if 0 in df.shape:
            raise ValueError(
                f"One dataframe is empty with shape={df.shape!r}.")
        return df


    case = data_covid_france_departments_tests(metropole=True)
    case.tail()






.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    somewhereaftercovid_39_std/aftercovid/aftercovid/data/pandas_cache.py:24: DtypeWarning: Columns (0) have mixed types. Specify dtype option on import or set low_memory=False.
      df = pandas.read_csv(url, **kwargs)  # pragma: no cover


.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <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>dep</th>
          <th>jour</th>
          <th>cage10</th>
          <th>cl_age90</th>
          <th>pop</th>
          <th>P</th>
          <th>T</th>
          <th>Ti</th>
          <th>Tp</th>
          <th>Td</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>1178040</th>
          <td>95</td>
          <td>2023-06-23</td>
          <td>[90-Inf)</td>
          <td>90</td>
          <td>10176.0</td>
          <td>0.0</td>
          <td>2.0</td>
          <td>0.00</td>
          <td>0.0</td>
          <td>19.65</td>
        </tr>
        <tr>
          <th>1178041</th>
          <td>95</td>
          <td>2023-06-24</td>
          <td>[90-Inf)</td>
          <td>90</td>
          <td>10176.0</td>
          <td>0.0</td>
          <td>5.0</td>
          <td>0.00</td>
          <td>0.0</td>
          <td>49.14</td>
        </tr>
        <tr>
          <th>1178042</th>
          <td>95</td>
          <td>2023-06-25</td>
          <td>[90-Inf)</td>
          <td>90</td>
          <td>10176.0</td>
          <td>0.0</td>
          <td>4.0</td>
          <td>0.00</td>
          <td>0.0</td>
          <td>39.31</td>
        </tr>
        <tr>
          <th>1178043</th>
          <td>95</td>
          <td>2023-06-26</td>
          <td>[90-Inf)</td>
          <td>90</td>
          <td>10176.0</td>
          <td>0.0</td>
          <td>8.0</td>
          <td>0.00</td>
          <td>0.0</td>
          <td>78.62</td>
        </tr>
        <tr>
          <th>1178044</th>
          <td>95</td>
          <td>2023-06-27</td>
          <td>[90-Inf)</td>
          <td>90</td>
          <td>10176.0</td>
          <td>1.0</td>
          <td>5.0</td>
          <td>9.83</td>
          <td>20.0</td>
          <td>49.14</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 59-60

Aggrégation par départements et par jour.

.. GENERATED FROM PYTHON SOURCE LINES 60-67

.. code-block:: default


    case = valid(
        case[case.cl_age90 != 0].groupby(['dep', 'jour'], as_index=False).sum()
    )
    case.tail()







.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <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>dep</th>
          <th>jour</th>
          <th>cage10</th>
          <th>cl_age90</th>
          <th>pop</th>
          <th>P</th>
          <th>T</th>
          <th>Ti</th>
          <th>Tp</th>
          <th>Td</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>109531</th>
          <td>95</td>
          <td>2023-06-23</td>
          <td>[00-10)[10-20)[20-30)[30-40)[40-50)[50-60)[60-...</td>
          <td>531</td>
          <td>1276534.0</td>
          <td>15.0</td>
          <td>246.0</td>
          <td>12.28</td>
          <td>50.65</td>
          <td>241.91</td>
        </tr>
        <tr>
          <th>109532</th>
          <td>95</td>
          <td>2023-06-24</td>
          <td>[00-10)[10-20)[20-30)[30-40)[40-50)[50-60)[60-...</td>
          <td>531</td>
          <td>1276534.0</td>
          <td>10.0</td>
          <td>218.0</td>
          <td>8.16</td>
          <td>33.51</td>
          <td>219.56</td>
        </tr>
        <tr>
          <th>109533</th>
          <td>95</td>
          <td>2023-06-25</td>
          <td>[00-10)[10-20)[20-30)[30-40)[40-50)[50-60)[60-...</td>
          <td>531</td>
          <td>1276534.0</td>
          <td>2.0</td>
          <td>68.0</td>
          <td>1.28</td>
          <td>28.57</td>
          <td>95.36</td>
        </tr>
        <tr>
          <th>109534</th>
          <td>95</td>
          <td>2023-06-26</td>
          <td>[00-10)[10-20)[20-30)[30-40)[40-50)[50-60)[60-...</td>
          <td>531</td>
          <td>1276534.0</td>
          <td>20.0</td>
          <td>346.0</td>
          <td>18.06</td>
          <td>51.18</td>
          <td>348.35</td>
        </tr>
        <tr>
          <th>109535</th>
          <td>95</td>
          <td>2023-06-27</td>
          <td>[00-10)[10-20)[20-30)[30-40)[40-50)[50-60)[60-...</td>
          <td>531</td>
          <td>1276534.0</td>
          <td>16.0</td>
          <td>264.0</td>
          <td>20.01</td>
          <td>70.38</td>
          <td>267.95</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 68-69

Quelques aggrégations, par département.

.. GENERATED FROM PYTHON SOURCE LINES 69-73

.. code-block:: default


    deps = valid(case.groupby(["dep", "jour"], as_index=False).sum())
    deps.tail(n=10)






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <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>dep</th>
          <th>jour</th>
          <th>cage10</th>
          <th>cl_age90</th>
          <th>pop</th>
          <th>P</th>
          <th>T</th>
          <th>Ti</th>
          <th>Tp</th>
          <th>Td</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>109526</th>
          <td>95</td>
          <td>2023-06-18</td>
          <td>[00-10)[10-20)[20-30)[30-40)[40-50)[50-60)[60-...</td>
          <td>531</td>
          <td>1276534.0</td>
          <td>4.0</td>
          <td>92.0</td>
          <td>2.55</td>
          <td>31.94</td>
          <td>126.94</td>
        </tr>
        <tr>
          <th>109527</th>
          <td>95</td>
          <td>2023-06-19</td>
          <td>[00-10)[10-20)[20-30)[30-40)[40-50)[50-60)[60-...</td>
          <td>531</td>
          <td>1276534.0</td>
          <td>22.0</td>
          <td>407.0</td>
          <td>29.09</td>
          <td>67.27</td>
          <td>387.99</td>
        </tr>
        <tr>
          <th>109528</th>
          <td>95</td>
          <td>2023-06-20</td>
          <td>[00-10)[10-20)[20-30)[30-40)[40-50)[50-60)[60-...</td>
          <td>531</td>
          <td>1276534.0</td>
          <td>13.0</td>
          <td>339.0</td>
          <td>9.32</td>
          <td>32.87</td>
          <td>325.92</td>
        </tr>
        <tr>
          <th>109529</th>
          <td>95</td>
          <td>2023-06-21</td>
          <td>[00-10)[10-20)[20-30)[30-40)[40-50)[50-60)[60-...</td>
          <td>531</td>
          <td>1276534.0</td>
          <td>14.0</td>
          <td>302.0</td>
          <td>14.96</td>
          <td>41.79</td>
          <td>323.51</td>
        </tr>
        <tr>
          <th>109530</th>
          <td>95</td>
          <td>2023-06-22</td>
          <td>[00-10)[10-20)[20-30)[30-40)[40-50)[50-60)[60-...</td>
          <td>531</td>
          <td>1276534.0</td>
          <td>20.0</td>
          <td>251.0</td>
          <td>15.73</td>
          <td>67.11</td>
          <td>243.76</td>
        </tr>
        <tr>
          <th>109531</th>
          <td>95</td>
          <td>2023-06-23</td>
          <td>[00-10)[10-20)[20-30)[30-40)[40-50)[50-60)[60-...</td>
          <td>531</td>
          <td>1276534.0</td>
          <td>15.0</td>
          <td>246.0</td>
          <td>12.28</td>
          <td>50.65</td>
          <td>241.91</td>
        </tr>
        <tr>
          <th>109532</th>
          <td>95</td>
          <td>2023-06-24</td>
          <td>[00-10)[10-20)[20-30)[30-40)[40-50)[50-60)[60-...</td>
          <td>531</td>
          <td>1276534.0</td>
          <td>10.0</td>
          <td>218.0</td>
          <td>8.16</td>
          <td>33.51</td>
          <td>219.56</td>
        </tr>
        <tr>
          <th>109533</th>
          <td>95</td>
          <td>2023-06-25</td>
          <td>[00-10)[10-20)[20-30)[30-40)[40-50)[50-60)[60-...</td>
          <td>531</td>
          <td>1276534.0</td>
          <td>2.0</td>
          <td>68.0</td>
          <td>1.28</td>
          <td>28.57</td>
          <td>95.36</td>
        </tr>
        <tr>
          <th>109534</th>
          <td>95</td>
          <td>2023-06-26</td>
          <td>[00-10)[10-20)[20-30)[30-40)[40-50)[50-60)[60-...</td>
          <td>531</td>
          <td>1276534.0</td>
          <td>20.0</td>
          <td>346.0</td>
          <td>18.06</td>
          <td>51.18</td>
          <td>348.35</td>
        </tr>
        <tr>
          <th>109535</th>
          <td>95</td>
          <td>2023-06-27</td>
          <td>[00-10)[10-20)[20-30)[30-40)[40-50)[50-60)[60-...</td>
          <td>531</td>
          <td>1276534.0</td>
          <td>16.0</td>
          <td>264.0</td>
          <td>20.01</td>
          <td>70.38</td>
          <td>267.95</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 74-75

Sur tout le territoire.

.. GENERATED FROM PYTHON SOURCE LINES 75-79

.. code-block:: default


    france = case.groupby(["jour"], as_index=False).sum()
    france.tail(n=10)






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <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>jour</th>
          <th>dep</th>
          <th>cage10</th>
          <th>cl_age90</th>
          <th>pop</th>
          <th>P</th>
          <th>T</th>
          <th>Ti</th>
          <th>Tp</th>
          <th>Td</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>1131</th>
          <td>2023-06-18</td>
          <td>0102030405060708091011121314151617181921222324...</td>
          <td>[00-10)[10-20)[20-30)[30-40)[40-50)[50-60)[60-...</td>
          <td>50976</td>
          <td>65627454.0</td>
          <td>230.0</td>
          <td>4134.0</td>
          <td>396.55</td>
          <td>3839.41</td>
          <td>8312.51</td>
        </tr>
        <tr>
          <th>1132</th>
          <td>2023-06-19</td>
          <td>0102030405060708091011121314151617181921222324...</td>
          <td>[00-10)[10-20)[20-30)[30-40)[40-50)[50-60)[60-...</td>
          <td>50938</td>
          <td>65613544.0</td>
          <td>1796.0</td>
          <td>21846.0</td>
          <td>2604.81</td>
          <td>7639.19</td>
          <td>35226.84</td>
        </tr>
        <tr>
          <th>1133</th>
          <td>2023-06-20</td>
          <td>0102030405060708091011121314151617181921222324...</td>
          <td>[00-10)[10-20)[20-30)[30-40)[40-50)[50-60)[60-...</td>
          <td>50938</td>
          <td>65613544.0</td>
          <td>1378.0</td>
          <td>17154.0</td>
          <td>2365.26</td>
          <td>7736.85</td>
          <td>28971.69</td>
        </tr>
        <tr>
          <th>1134</th>
          <td>2023-06-21</td>
          <td>0102030405060708091011121314151617181921222324...</td>
          <td>[00-10)[10-20)[20-30)[30-40)[40-50)[50-60)[60-...</td>
          <td>50948</td>
          <td>65609313.0</td>
          <td>1160.0</td>
          <td>14704.0</td>
          <td>1877.49</td>
          <td>7682.57</td>
          <td>24634.95</td>
        </tr>
        <tr>
          <th>1135</th>
          <td>2023-06-22</td>
          <td>0102030405060708091011121314151617181921222324...</td>
          <td>[00-10)[10-20)[20-30)[30-40)[40-50)[50-60)[60-...</td>
          <td>50967</td>
          <td>65620566.0</td>
          <td>1001.0</td>
          <td>11556.0</td>
          <td>1631.53</td>
          <td>7796.87</td>
          <td>19556.14</td>
        </tr>
        <tr>
          <th>1136</th>
          <td>2023-06-23</td>
          <td>0102030405060708091011121314151617181921222324...</td>
          <td>[00-10)[10-20)[20-30)[30-40)[40-50)[50-60)[60-...</td>
          <td>50967</td>
          <td>65620566.0</td>
          <td>1011.0</td>
          <td>11962.0</td>
          <td>1627.93</td>
          <td>7392.79</td>
          <td>19878.85</td>
        </tr>
        <tr>
          <th>1137</th>
          <td>2023-06-24</td>
          <td>0102030405060708091011121314151617181921222324...</td>
          <td>[00-10)[10-20)[20-30)[30-40)[40-50)[50-60)[60-...</td>
          <td>50967</td>
          <td>65620566.0</td>
          <td>558.0</td>
          <td>11145.0</td>
          <td>964.71</td>
          <td>4932.64</td>
          <td>18726.24</td>
        </tr>
        <tr>
          <th>1138</th>
          <td>2023-06-25</td>
          <td>0102030405060708091011121314151617181921222324...</td>
          <td>[00-10)[10-20)[20-30)[30-40)[40-50)[50-60)[60-...</td>
          <td>50976</td>
          <td>65627454.0</td>
          <td>184.0</td>
          <td>3495.0</td>
          <td>319.92</td>
          <td>3441.66</td>
          <td>7484.84</td>
        </tr>
        <tr>
          <th>1139</th>
          <td>2023-06-26</td>
          <td>0102030405060708091011121314151617181921222324...</td>
          <td>[00-10)[10-20)[20-30)[30-40)[40-50)[50-60)[60-...</td>
          <td>50887</td>
          <td>65622747.0</td>
          <td>1434.0</td>
          <td>18864.0</td>
          <td>2295.85</td>
          <td>7380.86</td>
          <td>30830.98</td>
        </tr>
        <tr>
          <th>1140</th>
          <td>2023-06-27</td>
          <td>0102030405060708091011121314151617181921222324...</td>
          <td>[00-10)[10-20)[20-30)[30-40)[40-50)[50-60)[60-...</td>
          <td>50887</td>
          <td>65622747.0</td>
          <td>972.0</td>
          <td>13827.0</td>
          <td>1606.70</td>
          <td>6494.00</td>
          <td>23537.51</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 80-82

Calcul du R
+++++++++++

.. GENERATED FROM PYTHON SOURCE LINES 82-98

.. code-block:: default



    def compute_r(df, col_day='jour', col='P', last_day=None):
        if last_day is None:
            last_day = df.jour.max()
        v1 = last_day - Timedelta(days=6)
        v2 = last_day
        p1 = last_day - Timedelta(days=13)
        p2 = last_day - Timedelta(days=7)
        w1 = df[(df[col_day] >= p1) & (df[col_day] <= p2)]
        w2 = df[(df[col_day] >= v1) & (df[col_day] <= v2)]
        return w2[col].sum() / w1[col].sum()


    compute_r(france)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    0.7627323195751871



.. GENERATED FROM PYTHON SOURCE LINES 99-104

On regarde quelle tête ça a sur six mois.
Ce n'est pas le code le plus efficace mais c'est rapide à
écrire. Dans le cas idéal, il faudra s'assurer que toutes
les dates sont présents et les compléter le cas échéants
puis calculer l'estimateur sur des fenêtres glissantes.

.. GENERATED FROM PYTHON SOURCE LINES 104-114

.. code-block:: default


    last_day = france.jour.max()
    obs = []
    for i in range(0, 180):
        ld = last_day - Timedelta(days=i)
        obs.append({'jour': ld, 'R': compute_r(france, last_day=ld)})

    gr = DataFrame(obs).sort_values("jour")
    gr.tail()






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <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>jour</th>
          <th>R</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>4</th>
          <td>2023-06-23</td>
          <td>0.791458</td>
        </tr>
        <tr>
          <th>3</th>
          <td>2023-06-24</td>
          <td>0.778821</td>
        </tr>
        <tr>
          <th>2</th>
          <td>2023-06-25</td>
          <td>0.778816</td>
        </tr>
        <tr>
          <th>1</th>
          <td>2023-06-26</td>
          <td>0.783825</td>
        </tr>
        <tr>
          <th>0</th>
          <td>2023-06-27</td>
          <td>0.762732</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 115-116

Et on dessine.

.. GENERATED FROM PYTHON SOURCE LINES 116-120

.. code-block:: default


    gr.set_index('jour').plot(title="Evolution de R en Métropole")





.. image-sg:: /auto_examples/images/sphx_glr_plot_covid_r0_001.png
   :alt: Evolution de R en Métropole
   :srcset: /auto_examples/images/sphx_glr_plot_covid_r0_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 121-125

Carte du R par département
++++++++++++++++++++++++++

On calcule les R par départements.

.. GENERATED FROM PYTHON SOURCE LINES 125-134

.. code-block:: default


    obs = []
    for d in set(deps.dep):
        r = compute_r(deps[deps.dep == d])
        obs.append({'dep': d, 'R': r})

    depdf = DataFrame(obs).sort_values("dep")
    depdf.tail()






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <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>dep</th>
          <th>R</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>46</th>
          <td>91</td>
          <td>0.829268</td>
        </tr>
        <tr>
          <th>51</th>
          <td>92</td>
          <td>0.825893</td>
        </tr>
        <tr>
          <th>49</th>
          <td>93</td>
          <td>0.741722</td>
        </tr>
        <tr>
          <th>95</th>
          <td>94</td>
          <td>0.742105</td>
        </tr>
        <tr>
          <th>25</th>
          <td>95</td>
          <td>0.906542</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 135-136

On récupère les contours des départements français.

.. GENERATED FROM PYTHON SOURCE LINES 136-140

.. code-block:: default


    loc = data_france_departments(metropole=True)
    loc.tail()






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <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>ancienne_re</th>
          <th>code_ancien</th>
          <th>code_depart</th>
          <th>code_region</th>
          <th>departement</th>
          <th>region</th>
          <th>geometry</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>96</th>
          <td>Limousin</td>
          <td>74.0</td>
          <td>87</td>
          <td>75.0</td>
          <td>Haute-Vienne</td>
          <td>Nouvelle-Aquitaine</td>
          <td>POLYGON ((0.92278 45.94610, 0.92856 45.94808, ...</td>
        </tr>
        <tr>
          <th>97</th>
          <td>Auvergne</td>
          <td>83.0</td>
          <td>63</td>
          <td>84.0</td>
          <td>Puy-de-Dôme</td>
          <td>Auvergne-Rhône-Alpes</td>
          <td>POLYGON ((2.45492 45.76131, 2.45169 45.76137, ...</td>
        </tr>
        <tr>
          <th>98</th>
          <td>Basse-Normandie</td>
          <td>25.0</td>
          <td>14</td>
          <td>28.0</td>
          <td>Calvados</td>
          <td>Normandie</td>
          <td>MULTIPOLYGON (((-0.42990 48.86405, -0.42881 48...</td>
        </tr>
        <tr>
          <th>99</th>
          <td>Rhône-Alpes</td>
          <td>82.0</td>
          <td>07</td>
          <td>84.0</td>
          <td>Ardèche</td>
          <td>Auvergne-Rhône-Alpes</td>
          <td>POLYGON ((4.30746 44.98596, 4.30412 44.98837, ...</td>
        </tr>
        <tr>
          <th>100</th>
          <td>Midi-Pyrénées</td>
          <td>73.0</td>
          <td>32</td>
          <td>76.0</td>
          <td>Gers</td>
          <td>Occitanie</td>
          <td>POLYGON ((-0.24307 43.66404, -0.24328 43.66476...</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 141-142

On fusionne avec les R.

.. GENERATED FROM PYTHON SOURCE LINES 142-146

.. code-block:: default


    locdep = GeoDataFrame(depdf.merge(loc, left_on='dep', right_on='code_depart'))
    locdep.tail()






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <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>dep</th>
          <th>R</th>
          <th>ancienne_re</th>
          <th>code_ancien</th>
          <th>code_depart</th>
          <th>code_region</th>
          <th>departement</th>
          <th>region</th>
          <th>geometry</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>91</th>
          <td>91</td>
          <td>0.829268</td>
          <td>Île-de-France</td>
          <td>11.0</td>
          <td>91</td>
          <td>11.0</td>
          <td>Essonne</td>
          <td>Île-de-France</td>
          <td>POLYGON ((2.04261 48.62638, 2.04045 48.62754, ...</td>
        </tr>
        <tr>
          <th>92</th>
          <td>92</td>
          <td>0.825893</td>
          <td>Île-de-France</td>
          <td>11.0</td>
          <td>92</td>
          <td>11.0</td>
          <td>Hauts-de-Seine</td>
          <td>Île-de-France</td>
          <td>POLYGON ((2.22040 48.92061, 2.23114 48.92773, ...</td>
        </tr>
        <tr>
          <th>93</th>
          <td>93</td>
          <td>0.741722</td>
          <td>Île-de-France</td>
          <td>11.0</td>
          <td>93</td>
          <td>11.0</td>
          <td>Seine-Saint-Denis</td>
          <td>Île-de-France</td>
          <td>POLYGON ((2.45949 48.95505, 2.46072 48.95840, ...</td>
        </tr>
        <tr>
          <th>94</th>
          <td>94</td>
          <td>0.742105</td>
          <td>Île-de-France</td>
          <td>11.0</td>
          <td>94</td>
          <td>11.0</td>
          <td>Val-de-Marne</td>
          <td>Île-de-France</td>
          <td>POLYGON ((2.32906 48.81378, 2.33190 48.81701, ...</td>
        </tr>
        <tr>
          <th>95</th>
          <td>95</td>
          <td>0.906542</td>
          <td>Île-de-France</td>
          <td>11.0</td>
          <td>95</td>
          <td>11.0</td>
          <td>Val-d'Oise</td>
          <td>Île-de-France</td>
          <td>POLYGON ((2.43548 49.13411, 2.44084 49.13419, ...</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 147-149

Et on dessine. Les départements en vert sont ceux pour lequel
le R est > 1.

.. GENERATED FROM PYTHON SOURCE LINES 149-191

.. code-block:: default


    with warnings.catch_warnings():
        warnings.filterwarnings("ignore", category=UserWarning)
        warnings.filterwarnings("ignore", category=DeprecationWarning)
        fig, axs = plt.subplots(
            1, 2, figsize=(16, 10),
            gridspec_kw={'width_ratios': [2, 1]})

        # métropole
        ax = axs[0]
        cax = make_axes_locatable(ax).append_axes("right", size="5%", pad=0.1)
        locdep.plot(
            column="R", ax=ax, edgecolor='black',
            legend=True, cax=cax, cmap="OrRd")
        if (locdep.R < 1).sum() > 0:
            locdep[locdep.R < 1].geometry.boundary.plot(
                color=None, edgecolor='g', linewidth=2, ax=ax, label="R<1")
        ax.set_title(f"R par départments de la métropole\n{deps.jour.max()!r}")

        for _, row in locdep.iterrows():
            p = row['geometry'].representative_point()
            ax.annotate(f"{row['R']:1.1f}", xy=(p.x, p.y),
                        horizontalalignment='center', color="black", fontsize=8)

        ax.legend()

        # Paris et sa région
        idf = set(['75', '77', '78', '91', '92', '93', '94', '95'])
        ax = axs[1]
        locdep2 = locdep[locdep.dep.isin(idf)]
        cax = make_axes_locatable(ax).append_axes("right", size="5%", pad=0.1)
        locdep2.plot(
            column="R", ax=ax, edgecolor='black',
            legend=True, cax=cax, cmap="OrRd")
        ax.set_title(f"R par départments de la métropole\n{deps.jour.max()!r}")

        for _, row in locdep2.iterrows():
            p = row['geometry'].representative_point()
            ax.annotate(f"{row['R']:1.1f}", xy=(p.x, p.y),
                        horizontalalignment='center', color="black", fontsize=8)

    plt.show()



.. image-sg:: /auto_examples/images/sphx_glr_plot_covid_r0_002.png
   :alt: R par départments de la métropole Timestamp('2023-06-27 00:00:00'), R par départments de la métropole Timestamp('2023-06-27 00:00:00')
   :srcset: /auto_examples/images/sphx_glr_plot_covid_r0_002.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    /usr/local/lib/python3.9/site-packages/geopandas/plotting.py:48: ShapelyDeprecationWarning: The 'type' attribute is deprecated, and will be removed in the future. You can use the 'geom_type' attribute instead.
      if geom is not None and geom.type.startswith(prefix) and not geom.is_empty:
    /usr/local/lib/python3.9/site-packages/geopandas/plotting.py:48: ShapelyDeprecationWarning: The 'type' attribute is deprecated, and will be removed in the future. You can use the 'geom_type' attribute instead.
      if geom is not None and geom.type.startswith(prefix) and not geom.is_empty:





.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  46.073 seconds)


.. _sphx_glr_download_auto_examples_plot_covid_r0.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example




    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_covid_r0.py <plot_covid_r0.py>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_covid_r0.ipynb <plot_covid_r0.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_