[WIP] fix bug in examples

dirty_cat/datasets/fetching.py

@@ -346,18 +346,23 @@ def fetch_employee_salaries():
     dict
         a dictionary containing:
 
-            - a short description of the dataset (under the ``description``
+            - a short description of the dataset (under the ``DESCR``
               key)
-            - an absolute path leading to the csv file where the data is stored
-              locally (under the ``path`` key)
+            - the tabular data (under the ``data`` key)
+            - the target (under the ``target`` key)
 
     References
     ----------
     https://catalog.data.gov/dataset/employee-salaries-2016
 
     """
 
-    return fetch_dataset(EMPLOYEE_SALARIES_CONFIG, show_progress=False)
+    from sklearn.datasets import fetch_openml
+    data = fetch_openml(data_id=42125, as_frame=True)
+    return data
+
+    # link dead.
+    # return fetch_dataset(EMPLOYEE_SALARIES_CONFIG, show_progress=False)
 
 
 def fetch_road_safety():

examples/02_fit_predict_plot_employee_salaries.py

@@ -21,16 +21,17 @@
 # We first download the dataset:
 from dirty_cat.datasets import fetch_employee_salaries
 employee_salaries = fetch_employee_salaries()
-print(employee_salaries['description'])
+print(employee_salaries['DESC'])
+
 
 ################################################################################
 # Then we load it:
 import pandas as pd
-df = pd.read_csv(employee_salaries['path']).astype(str)
+df = data = employee_salaries['data']
 
 # Test if load was unsuccesful
 if '"code" : "authentication_required"' in str(df.iloc[0]):
-    print('Error while loading the data') #raise IOError
+    raise IOError
 ################################################################################
 # Now, let's carry out some basic preprocessing:
 df['Current Annual Salary'] = df['Current Annual Salary'].str.strip('$').astype(