<jupyter_start><jupyter_text>📝 Exercise M1.01 Imagine we are interested in predicting penguins species based on two of theirbody measurements: culmen length and culmen depth. First we want to do somedata exploration to get a feel for the data.What are the features? What is the target? The data is located in `../datasets/penguins_classification.csv`, load it with`pandas` into a `DataFrame`.<jupyter_code># Write your code here.<jupyter_output><empty_output><jupyter_text>Show a few samples of the data.How many features are numerical? How many features are categorical?<jupyter_code># Write your code here.<jupyter_output><empty_output><jupyter_text>What are the different penguins species available in the dataset and how manysamples of each species are there? Hint: select the right column and use the[`value_counts`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.value_counts.html)method.<jupyter_code># Write your code here.<jupyter_output><empty_output><jupyter_text>Plot histograms for the numerical features<jupyter_code># Write your code here.<jupyter_output><empty_output><jupyter_text>Show features distribution for each class. Hint: use[`seaborn.pairplot`](https://seaborn.pydata.org/generated/seaborn.pairplot.html)<jupyter_code># Write your code here.<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.08767951625094482, 'fraction_numerical': 0.0022675736961451248, 'mean_word_length': 6.975903614457831, 'pattern_counts': {'":': 0, '<': 21, '<?xml version=': 0, '>': 21, 'https://': 2, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '5', 'dir': 'jupyter-structured-clean-dedup', 'id': '107643', 'license': 'CC-BY-4.0', 'n_tokens_mistral': 418, 'n_tokens_neox': 405, 'n_words': 127, 'path': 'notebooks/01_tabular_data_exploration_ex_01.ipynb', 'repo_name': 'leonsor/scikit-learn-mooc', 'size': '1323'}