Changing the simple_datasets_tutorial and deleting new dataset

examples/20_basic/simple_datasets_tutorial.py

@@ -3,11 +3,15 @@
 Datasets
 ========
 
-A basic tutorial on how to list and download datasets.
+A basic tutorial on how to list, load and visualize datasets.
 """
 ############################################################################
-import openml
+# In general, we recommend working with tasks, so that the results can
+# be easily reproduced. Furthermore, the results can be compared to existing results
+# at OpenML. However, for the purposes of this tutorial, we are going to work with
+# the datasets directly.
 
+import openml
 ############################################################################
 # List datasets
 # =============
@@ -19,11 +23,46 @@
 # Download a dataset
 # ==================
 
-first_dataset_id = int(datasets_df['did'].iloc[0])
-dataset = openml.datasets.get_dataset(first_dataset_id)
+# Iris dataset https://www.openml.org/d/61
+dataset = openml.datasets.get_dataset(61)
 
 # Print a summary
 print(f"This is dataset '{dataset.name}', the target feature is "
       f"'{dataset.default_target_attribute}'")
 print(f"URL: {dataset.url}")
 print(dataset.description[:500])
+
+############################################################################
+# Load a dataset
+# ==============
+
+# X - An array/dataframe where each row represents one example with
+# the corresponding feature values.
+# y - the classes for each example
+# categorical_indicator - an array that indicates which feature is categorical
+# attribute_names - the names of the features for the examples (X) and
+# target feature (y)
+X, y, categorical_indicator, attribute_names = dataset.get_data(
+    dataset_format='dataframe',
+    target=dataset.default_target_attribute
+)
+############################################################################
+# Visualize the dataset
+# =====================
+
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+sns.set_style("darkgrid")
+
+
+def hide_current_axis():
+    plt.gca().set_visible(False)
+
+
+# We combine all the data so that we can map the different
+# examples to different colors according to the classes.
+combined_data = pd.concat([X, y], axis=1)
+iris_plot = sns.pairplot(combined_data, hue="class")
+iris_plot.map_upper(hide_current_axis)
+plt.show()