# %% [markdown]

# A basic tutorial on how to list, load and visualize datasets.

#

# 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

# %% [markdown]

# ## List datasets stored on OpenML

# %%

datasets_df = openml.datasets.list_datasets()

print(datasets_df.head(n=10))

# %% [markdown]

# ## Download a dataset

# %%

# Iris dataset https://www.openml.org/d/61

dataset = openml.datasets.get_dataset(dataset_id=61)

# Print a summary

print(

f"This is dataset '{dataset.name}', the target feature is '{dataset.default_target_attribute}'"

)

print(f"URL: {dataset.url}")

print(dataset.description[:500])

# %% [markdown]

# ## Load a dataset

# * `X` - A dataframe where each row represents one example with

# the corresponding feature values.

# * `y` - the classes for each example

# * `categorical_indicator` - a list 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(

target=dataset.default_target_attribute

)

# %% [markdown]

# Visualize the dataset

# %%

import matplotlib.pyplot as plt

import pandas as pd

import seaborn as sns

iris_plot = sns.pairplot(pd.concat([X, y], axis=1), hue="class")

plt.show()