<jupyter_start><jupyter_text>https://stackoverflow.com/questions/10715965<jupyter_code>i = 0
for r in rows:
    datas = r.find_all('td')
    if not datas :
        continue
    df.loc[i] = [ datas[4].text, datas[8].text]
    i +=1
# https://www.google.com/search?q={Country}+Continent
df.loc[df.index.max() + 1] = ['Burma', 'AS']
df.loc[df.index.max() + 1] = ['Cape Verde', 'AF']
df.loc[df.index.max() + 1] = ['Czech Republic', 'EU']
df.loc[df.index.max() + 1] = ['Czechoslovakia', 'EU']
df.loc[df.index.max() + 1] = ['Côte d\'Ivoire', 'AF']
df.loc[df.index.max() + 1] = ['East Germany', 'EU']
df.loc[df.index.max() + 1] = ['Federal Republic of Yugoslavia', 'EU']
df.loc[df.index.max() + 1] = ['Holy See (Vatican City State)', 'EU']
df.loc[df.index.max() + 1] = ['Isle Of Man', 'EU']
df.loc[df.index.max() + 1] = ['Korea', 'AS']
df.loc[df.index.max() + 1] = ['North Vietnam', 'AS']
df.loc[df.index.max() + 1] = ['Republic of North Macedonia', 'EU']
df.loc[df.index.max() + 1] = ['Reunion', 'AF']
df.loc[df.index.max() + 1] = ['Soviet Union', 'EU']
df.loc[df.index.max() + 1] = ['Svalbard And Jan Mayen', 'EU']
df.loc[df.index.max() + 1] = ['Swaziland', 'AF']
df.loc[df.index.max() + 1] = ['The Democratic Republic Of Congo', 'AF']
df.loc[df.index.max() + 1] = ['UK', 'EU']
df.loc[df.index.max() + 1] = ['USA', 'NA']
df.loc[df.index.max() + 1] = ['West Germany', 'EU']
df.loc[df.index.max() + 1] = ['Yugoslavia', 'EU']
df.loc[df.index.max() + 1] = ['Zaire', 'AF']
df.loc[df.index.max() + 1] = ['No Country', 'idk']
# 'EU', Europe
# 'AS', Asia
# 'NA', north Americas
# 'AF', Africa
# 'AN', Antarctica
# 'SA', south Americas
# 'OC', Oceania
df
df.to_csv(Dataset_path +'Country2Continent.csv', index=False)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.2661662817551963, 'fraction_numerical': 0.020785219399538105, 'mean_word_length': 4.315950920245399, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 2, 'lorem ipsum': 0, 'www.': 1, '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': '603839', 'license': 'MIT', 'n_tokens_mistral': 804, 'n_tokens_neox': 734, 'n_words': 157, 'path': 'tmp/Country2Continent.ipynb', 'repo_name': 'mheidari98/Movie-Recommender-Systems', 'size': '1732'}