<jupyter_start><jupyter_text><jupyter_code>da= pd.DataFrame()
print(da)


dates=['April-10', 'April-11', 'April-12', 'April-13']
fruits=['Apple', 'Papaya', 'Banana', 'Mango']
prices=[3, 1, 2, 4]

da= pd.DataFrame({'Date':dates ,
                   'Fruit':fruits ,
                   'Price': prices})

print(da)


dates=['April-10', 'April-11', 'April-12', 'April-13']
fruits=['Apple', 'Papaya', 'Banana', 'Mango']
prices=[3, 1, 2, 4]

da = pd.DataFrame({'Date':dates ,
                   'Fruit':fruits ,
                   'Price': prices})

new_da=da.assign(Profit=6)
print(new_da)<jupyter_output>Date   Fruit  Price  Profit
0  April-10   Apple      3       6
1  April-11  Papaya      1       6
2  April-12  Banana      2       6
3  April-13   Mango      4       6
{'fraction_non_alphanumeric': 0.17838541666666666, 'fraction_numerical': 0.05859375, 'mean_word_length': 2.4026548672566372, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 4, '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': '514748', 'license': 'MIT', 'n_tokens_mistral': 343, 'n_tokens_neox': 306, 'n_words': 63, 'path': 'DateFrame.ipynb', 'repo_name': 'vanesamorale/PRACTICA5', 'size': '768'}