Modifie l'import du script (#187)

* urllib

* urllib request

* version module

* modularisation

* Automated changes

* Automated changes

* include fALSE

* Automated changes

* Automated changes

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>

content/course/modelisation/0_preprocessing.Rmd

@@ -97,13 +97,17 @@ par comté x année avec en colonnes les résultats de chaque candidat dans cet
 
 Si vous ne faites pas l'exercice 1, pensez à charger les données en executant la fonction `get_data.py` :
 
-```{python, echo = TRUE}
+```{python, echo = TRUE, include = FALSE}
 #!pip install geopandas
-import urllib
-import urllib.request
-code = 'https://raw.githubusercontent.com/linogaliana/python-datascientist/master/content/course/modelisation/get_data.py'
-code_import = urllib.request.urlopen(code).read()
-exec(code_import)
+
+import requests
+
+url = 'https://raw.githubusercontent.com/linogaliana/python-datascientist/test-preprocessing/content/course/modelisation/get_data.py'
+r = requests.get(url, allow_redirects=True)
+open('getdata.py', 'wb').write(r.content)
+
+import getdata
+votes = getdata.create_votes_dataframes()
 ```
 
 Ce code introduit une base nommée `votes` dans l'environnement. Il s'agit d'une

content/course/modelisation/1_modelevaluation.Rmd

@@ -55,13 +55,17 @@ Le code
 est disponible [sur Github](https://github.com/linogaliana/python-datascientist/blob/master/content/course/modelisation/get_data.py)
 
 
-```{python, echo = TRUE}
+```{python, echo = TRUE, include = FALSE}
 #!pip install geopandas
-import urllib
-import urllib.request
-code = 'https://raw.githubusercontent.com/linogaliana/python-datascientist/master/content/course/modelisation/get_data.py'
-code_import = urllib.request.urlopen(code).read()
-exec(code_import)
+
+import requests
+
+url = 'https://raw.githubusercontent.com/linogaliana/python-datascientist/test-preprocessing/content/course/modelisation/get_data.py'
+r = requests.get(url, allow_redirects=True)
+open('getdata.py', 'wb').write(r.content)
+
+import getdata
+votes = getdata.create_votes_dataframes()
 ```
 
 

content/course/modelisation/2_SVM.Rmd

@@ -54,13 +54,17 @@ Le code
 est disponible [sur Github](https://github.com/linogaliana/python-datascientist/blob/master/content/course/modelisation/get_data.py).
 
 
-```{python, echo = TRUE}
+```{python, echo = TRUE, include = FALSE}
 #!pip install geopandas
-import urllib
-import urllib.request
-code = 'https://raw.githubusercontent.com/linogaliana/python-datascientist/master/content/course/modelisation/get_data.py'
-code_import = urllib.request.urlopen(code).read()
-exec(code_import)
+
+import requests
+
+url = 'https://raw.githubusercontent.com/linogaliana/python-datascientist/test-preprocessing/content/course/modelisation/get_data.py'
+r = requests.get(url, allow_redirects=True)
+open('getdata.py', 'wb').write(r.content)
+
+import getdata
+votes = getdata.create_votes_dataframes()
 ```
 
 

content/course/modelisation/3_regression.Rmd

@@ -63,13 +63,17 @@ croisées à des variables socio-démographiques.
 Le code 
 est disponible [sur Github](https://github.com/linogaliana/python-datascientist/blob/master/content/course/modelisation/get_data.py).
 
-```{python, echo = TRUE}
+```{python, echo = TRUE, include = FALSE}
 #!pip install geopandas
-import urllib
-import urllib.request
-code = 'https://raw.githubusercontent.com/linogaliana/python-datascientist/master/content/course/modelisation/get_data.py'
-code_import = urllib.request.urlopen(code).read()
-exec(code_import)
+
+import requests
+
+url = 'https://raw.githubusercontent.com/linogaliana/python-datascientist/test-preprocessing/content/course/modelisation/get_data.py'
+r = requests.get(url, allow_redirects=True)
+open('getdata.py', 'wb').write(r.content)
+
+import getdata
+votes = getdata.create_votes_dataframes()
 ```
 
 

content/course/modelisation/4_featureselection.Rmd

@@ -57,13 +57,17 @@ Le code
 est disponible [sur Github](https://github.com/linogaliana/python-datascientist/blob/master/content/course/modelisation/get_data.py).
 
 
-```{python, echo = TRUE}
+```{python, echo = TRUE, include = FALSE}
 #!pip install geopandas
-import urllib
-import urllib.request
-code = 'https://raw.githubusercontent.com/linogaliana/python-datascientist/master/content/course/modelisation/get_data.py'
-code_import = urllib.request.urlopen(code).read()
-exec(code_import)
+
+import requests
+
+url = 'https://raw.githubusercontent.com/linogaliana/python-datascientist/test-preprocessing/content/course/modelisation/get_data.py'
+r = requests.get(url, allow_redirects=True)
+open('getdata.py', 'wb').write(r.content)
+
+import getdata
+votes = getdata.create_votes_dataframes()
 ```
 
 

content/course/modelisation/5_clustering.Rmd

@@ -91,13 +91,17 @@ Premièrement, nous pencher sur la plus intuitive: les k-means.
 
 # Données
 
-```{python, echo = TRUE}
+```{python, echo = TRUE, include = FALSE}
 #!pip install geopandas
-import urllib
-import urllib.request
-code = 'https://raw.githubusercontent.com/linogaliana/python-datascientist/master/content/course/modelisation/get_data.py'
-code_import = urllib.request.urlopen(code).read()
-exec(code_import)
+
+import requests
+
+url = 'https://raw.githubusercontent.com/linogaliana/python-datascientist/test-preprocessing/content/course/modelisation/get_data.py'
+r = requests.get(url, allow_redirects=True)
+open('getdata.py', 'wb').write(r.content)
+
+import getdata
+votes = getdata.create_votes_dataframes()
 ```
 
 ## Principe

content/course/modelisation/get_data.py

@@ -1,3 +1,5 @@
+import urllib
+import urllib.request
 import os
 import zipfile
 from urllib.request import Request, urlopen