<jupyter_start><jupyter_text>Now get some other names<jupyter_code>df4
#df2 = df[df["description"].str.contains("Greek:")]
df8 = df4[df4["des3"].str.contains(";", na=False)==False]
df8
# reset row index
df8 = df8.reset_index().drop(['index'], axis=1)
df8['des3']
tmp4 = pd.DataFrame(df8['des3'][:42].str.split(")", 1).tolist(), columns=["Names gr", "des4"])
tmp5 = pd.DataFrame(tmp4['Names gr'].str.split(",", 1).tolist(), columns=["Names gr", "rest"])

df9 = pd.concat([df8, tmp5], axis=1)
df8['des3'][1].split(")", 1)
df9 = df9.dropna()
df9 = df9.drop(['des3', 'rest'], axis=1)
df9<jupyter_output><empty_output><jupyter_text>1. Save File<jupyter_code># set csv output file_name
file_name = 'greek_rest.csv'
df9.to_csv(file_name, encoding='utf-8')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.2326478149100257, 'fraction_numerical': 0.05141388174807198, 'mean_word_length': 7.954022988505747, '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': '473537', 'license': 'MIT', 'n_tokens_mistral': 333, 'n_tokens_neox': 327, 'n_words': 54, 'path': 'greek.ipynb', 'repo_name': 'Erjon-19/Greek_names_preprocessing', 'size': '778'}