<jupyter_start><jupyter_text>concatenate both datasets at the common index 1<jupyter_code>new_data = pd.concat([data1, data2], 1)
new_data = new_data.drop(columns=['year'])
print(new_data)
new_data.plot()
insight = new_data.aFemale_1.value_counts()
insight.plot()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.13651877133105803, 'fraction_numerical': 0.017064846416382253, 'mean_word_length': 9.88888888888889, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, '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': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '548798', 'license': 'MIT', 'n_tokens_mistral': 120, 'n_tokens_neox': 115, 'n_words': 18, 'path': '{{ cookiecutter.repo_name }}/notebooks/all.ipynb', 'repo_name': 'alaadaff/Data-Science', 'size': '293'}