<jupyter_start><jupyter_text>We need to filter the na rows or columns. * Drop na of 'Series Name'<jupyter_code>df.dropna(subset=['Series Name'],axis=0,inplace=True)
df.tail()<jupyter_output><empty_output><jupyter_text>The na rows of (264~266) were deleted. * dropna columns if contain na<jupyter_code>df2 = df.dropna(how='any',axis=1)
df2.head()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.144, 'fraction_numerical': 0.02666666666666667, 'mean_word_length': 8.894736842105264, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, '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': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '105917', 'license': 'MIT', 'n_tokens_mistral': 151, 'n_tokens_neox': 146, 'n_words': 29, 'path': 'dropna.ipynb', 'repo_name': 'ahomer/pandas-go', 'size': '375'}