Digital Divide HW

Popcorn Hack: Example Problem in MCQ

Question: Which of the following actions are likely to be helpful in reducing

the digital divide? Select two answers.

Options

A. Designing new technologies intended only for advanced users

B. Designing new technologies to be accessible to individuals with different physical abilities

C. Implementing government regulations restricting citizens’ access to Web content

D. Having world governments support the construction of network infrastructure

CORRECT ANSWERS: B & D

Popcorn Hack 2

To fix the digital divide, we can expand access to affordable internet and technology,

offer digital literacy programs, and ensure underserved communities receive support.

Some efforts already include government-funded internet access and nonprofit initiatives.

We can add more public Wi-Fi spaces and increase tech training for all ages to

bridge the gap further.

HW Hack

added a loop to iterate through each row of the cleaned dataset, checking the value of

Rate (WB) (internet access percentage).

Depending on whether the percentage is above or below 70%, the program prints the country

name, the percentage, and either “doing great” or “needs improvement.”

The solution uses a simple conditional statement within a loop to generate the

desired output for each country.

import pandas as pd

data = pd.read_csv(“internet_users.csv”).drop(columns=[‘Notes’, ‘Year.2’, ‘Users (CIA)’, ‘Rate (ITU)’, ‘Year.1’]) # Drop extra columns: we will not be using these

data_cleaned = data.dropna() # Drop rows with NaN (aka blank) values

print(data_cleaned.head()) # Display the first few rows of the cleaned data

print(len(data)) # Check num of rows before removing blank rows

print(len(data_cleaned)) # Check num of rows after removing blank rows

Location Rate (WB) Year 0 World 67.4 2023.0 1 Afghanistan 18.4 2020.0 2 Albania 83.1 2023.0 3 Algeria 71.2 2022.0 5 Andorra 94.5 2022.0

y = data_cleaned[‘Rate (WB)’] # Take Percentage of the population using the internet from World Bank data in dataset name = data_cleaned[‘Location’] # take country name from WB data in dataset

[INSERT YOUR CODE HERE]

data_cleaned[‘Status’] = [‘doing great’ if rate > 70 else ‘needs improvement’ for rate in y]

Print the results

for country, rate, status in zip(name, y, data_cleaned[‘Status’]): print(f”{country}: {rate}%: {status}”)

World: 67.4%: needs improvement Afghanistan: 18.4%: needs improvement Albania: 83.1%: doing great Algeria: 71.2%: doing great Andorra: 94.5%: doing great Angola: 39.3%: needs improvement Antigua and Barbuda: 91.4%: doing great Argentina: 89.2%: doing great Armenia: 78.6%: doing great Aruba: 97.2%: doing great Australia: 95.0%: doing great Austria: 95.3%: doing great Azerbaijan: 88.0%: doing great Bahamas: 94.4%: doing great Bahrain: 100.0%: doing great Bangladesh: 44.5%: needs improvement Barbados: 76.2%: doing great Belarus: 91.5%: doing great Belgium: 94.6%: doing great Belize: 70.4%: doing great Benin: 33.8%: needs improvement Bermuda: 98.4%: doing great Bhutan: 86.8%: doing great Bolivia: 73.3%: doing great Bosnia and Herzegovina: 83.4%: doing great Botswana: 77.3%: doing great Brazil: 84.2%: doing great British Virgin Islands: 77.7%: doing great Brunei: 99.0%: doing great Bulgaria: 80.4%: doing great Burkina Faso: 19.9%: needs improvement Burundi: 11.3%: needs improvement Cambodia: 56.7%: needs improvement Cameroon: 43.9%: needs improvement Canada: 94.6%: doing great Cape Verde: 72.1%: doing great 2 Islands: 81.1%: doing great Central African Republic: 10.6%: needs improvement Chad: 12.2%: needs improvement Chile: 94.1%: doing great China: 77.5%: doing great Colombia: 73.0%: doing great Comoros: 27.3%: needs improvement Costa Rica: 85.1%: doing great Croatia: 83.2%: doing great Cuba: 73.2%: doing great Curacao: 68.1%: needs improvement Cyprus: 91.2%: doing great Czech Republic: 86.0%: doing great Democratic Republic of the Congo: 27.2%: needs improvement Denmark: 98.9%: doing great Djibouti: 65.0%: needs improvement Dominica: 83.4%: doing great Dominican Republic: 85.2%: doing great East Timor: 40.8%: needs improvement Ecuador: 72.7%: doing great Egypt: 72.2%: doing great El Salvador: 62.9%: needs improvement Equatorial Guinea: 66.8%: needs improvement Eritrea: 26.6%: needs improvement Estonia: 93.2%: doing great Eswatini: 58.3%: needs improvement Ethiopia: 19.4%: needs improvement Faroe Islands: 97.6%: doing great Fiji: 85.2%: doing great Finland: 93.5%: doing great France: 86.8%: doing great French Polynesia: 72.7%: doing great Gabon: 73.7%: doing great Gambia: 54.2%: needs improvement Georgia: 81.9%: doing great Germany: 92.5%: doing great Ghana: 69.8%: needs improvement Gibraltar: 94.4%: doing great Greece: 85.0%: doing great Greenland: 69.5%: needs improvement Grenada: 79.9%: doing great Guam: 80.5%: doing great Guatemala: 54.4%: needs improvement Guinea: 33.9%: needs improvement Guinea-Bissau: 31.6%: needs improvement Guyana: 85.3%: doing great Haiti: 39.3%: needs improvement Honduras: 59.7%: needs improvement Hong Kong: 95.6%: doing great Hungary: 91.5%: doing great Iceland: 99.9%: doing great India: 43.4%: needs improvement Indonesia: 69.2%: needs improvement Iran: 81.7%: doing great Iraq: 78.7%: doing great Ireland: 95.6%: doing great Israel: 91.9%: doing great Italy: 87.0%: doing great Ivory Coast: 43.8%: needs improvement Jamaica: 85.1%: doing great Japan: 93.2%: doing great Jordan: 90.5%: doing great Kazakhstan: 92.9%: doing great Kenya: 40.8%: needs improvement Kiribati: 54.4%: needs improvement Kosovo: 89.4%: doing great Kuwait: 99.8%: doing great Kyrgyzstan: 79.8%: doing great Laos: 66.2%: needs improvement Latvia: 92.2%: doing great Lebanon: 90.1%: doing great Lesotho: 47.0%: needs improvement Liberia: 30.1%: needs improvement Libya: 88.4%: doing great Liechtenstein: 99.6%: doing great Lithuania: 88.5%: doing great Luxembourg: 99.4%: doing great Macao: 89.8%: doing great Madagascar: 20.6%: needs improvement Malawi: 27.7%: needs improvement Malaysia: 97.7%: doing great Maldives: 85.2%: doing great Mali: 33.1%: needs improvement Malta: 91.9%: doing great Marshall Islands: 73.2%: doing great Mauritania: 44.4%: needs improvement Mauritius: 75.5%: doing great Mexico: 81.2%: doing great Micronesia: 40.5%: needs improvement Moldova: 71.0%: doing great Monaco: 98.6%: doing great Mongolia: 83.9%: doing great Montenegro: 88.2%: doing great Morocco: 89.9%: doing great Mozambique: 21.2%: needs improvement Myanmar: 48.1%: needs improvement Namibia: 62.2%: needs improvement Nauru: 83.3%: doing great Nepal: 49.6%: needs improvement Netherlands: 97.0%: doing great New Caledonia: 82.0%: doing great New Zealand: 95.7%: doing great Nicaragua: 61.1%: needs improvement Niger: 16.9%: needs improvement Nigeria: 35.5%: needs improvement North Macedonia: 84.2%: doing great Norway: 99.0%: doing great Oman: 97.9%: doing great Pakistan: 33.0%: needs improvement Palestine: 88.7%: doing great Panama: 73.6%: doing great Papua New Guinea: 27.0%: needs improvement Paraguay: 78.1%: doing great Peru: 74.7%: doing great Philippines: 75.2%: doing great Poland: 86.9%: doing great Portugal: 85.8%: doing great Puerto Rico: 87.3%: doing great Qatar: 100.0%: doing great Republic of the Congo: 36.3%: needs improvement Romania: 89.2%: doing great Russia: 92.3%: doing great Rwanda: 34.4%: needs improvement Saint Kitts and Nevis: 76.5%: doing great Saint Lucia: 75.8%: doing great Saint Vincent and the Grenadines: 78.7%: doing great Samoa: 76.3%: doing great San Marino: 85.1%: doing great Sao Tome and Principe: 57.0%: needs improvement Saudi Arabia: 100.0%: doing great Senegal: 60.0%: needs improvement Serbia: 85.4%: doing great Seychelles: 86.7%: doing great Sierra Leone: 30.4%: needs improvement Singapore: 96.9%: doing great Slovakia: 89.9%: doing great Slovenia: 90.4%: doing great Solomon Islands: 47.3%: needs improvement Somalia: 27.6%: needs improvement South Africa: 74.7%: doing great South Korea: 97.6%: doing great South Sudan: 12.1%: needs improvement Spain: 95.5%: doing great Sri Lanka: 50.1%: needs improvement Sudan: 28.7%: needs improvement Suriname: 75.8%: doing great Sweden: 95.7%: doing great Switzerland: 97.3%: doing great Syria: 35.8%: needs improvement Tajikistan: 36.1%: needs improvement Tanzania: 31.9%: needs improvement Thailand: 89.5%: doing great Togo: 37.6%: needs improvement Tonga: 66.7%: needs improvement Trinidad and Tobago: 80.0%: doing great Tunisia: 73.8%: doing great Turkey: 86.0%: doing great Turkmenistan: 21.3%: needs improvement Tuvalu: 82.3%: doing great Uganda: 10.0%: needs improvement Ukraine: 79.2%: doing great United Arab Emirates: 100.0%: doing great United Kingdom: 95.3%: doing great United States: 97.1%: doing great Uruguay: 89.9%: doing great US Virgin Islands: 64.4%: needs improvement Uzbekistan: 89.0%: doing great Vanuatu: 69.9%: needs improvement Venezuela: 61.6%: needs improvement Vietnam: 78.6%: doing great Yemen: 26.7%: needs improvement Zambia: 31.2%: needs improvement Zimbabwe: 32.6%: needs improvement

/tmp/ipykernel_198745/3944393366.py:5: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy data_cleaned[‘Status’] = [‘doing great’ if rate > 70 else ‘needs improvement’ for rate in y]