<jupyter_start><jupyter_text>INPUT names of boys INPUT correspoding ages INPUT corresponding scores PRINT generated table INPUT names of girls INPUT correspoding ages INPUT corresponding scores PRINT generated table<jupyter_code>import pandas as pd
boys= {
    "names": ["Charles","Jude","James","Kelvin","Prosper"],
    "age" : [15,16,16,17,14],
    "height":[5.7,5.9,5.8,6.1,5.9],
    "scores":[85,74,87,75,68]
}
girls= {
    "names": ["Ann","Jane","Joanne","Clare","Elizabeth"],
     "ages": [14,16,14,17,16],
    "height": [5.5,6,5.4,5.9,5.6],
    "scores" : [80,85,70,60,76]
}
boysoutput=pd.DataFrame(boys,index=["","","","",""])
girlsoutput=pd.DataFrame(girls,index=["","","","",""])
print(boysoutput)
print(girlsoutput)
#boysoutput.to_excel(C:\Users\danob\Desktop\danielcsc102\pythonass.xlsx)
# determining the name of the file
file_name = 'pythonass.xlsx'
  
# saving the excel
boysoutput.to_excel(file_name)
girlsoutput.to_excel(file_name)
print('DataFrame is written to Excel File successfully.')

import pandas 

data = {
  "calories": [420, 380, 390],
  "duration": [50, 40, 45]
}

#load data into a DataFrame object:
df = pandas.DataFrame(data)

print(df)<jupyter_output>calories  duration
0       420        50
1       380        40
2       390        45
{'fraction_non_alphanumeric': 0.17586750788643532, 'fraction_numerical': 0.0749211356466877, 'mean_word_length': 4.742081447963801, 'pattern_counts': {'":': 8, '<': 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': '649990', 'license': 'MIT', 'n_tokens_mistral': 537, 'n_tokens_neox': 474, 'n_words': 100, 'path': 'week 4 takeaway.ipynb', 'repo_name': 'obere123/danielcsc102', 'size': '1268'}