Digital Divide Homework and Popcorn hacks

Popcorn Hack 1:

Answer:

B Designing new technologies to be accessible to individuals with different physical abilities
D Having world governments support the construction of network infrastructure

Explanation:

B focuses on inclusivity, ensuring that technology is usable by people with varying abilities, which helps bridge gaps in accessibility.
D involves improving the infrastructure, making it possible for more people to access the internet and technology, which directly addresses the digital divide.

Popcorn Hack 2:

Answer:

To reduce the digital divide in our community, I would focus on improving access to affordable internet and technology, especially for underserved populations. Governments and organizations like nonprofits are already working to expand broadband infrastructure and provide low-cost devices for those who cannot afford them. Additionally, community programs that offer digital literacy training could be expanded to help individuals of all ages and backgrounds develop the skills needed to navigate the digital world. Adding more local tech hubs where people can access technology and receive training would also be an effective way to prevent the digital divide from growing.

Summary of the solution:

This solution addresses the digital divide by promoting accessibility, affordability, and digital literacy. Efforts to improve network infrastructure and provide resources for underserved communities are crucial. Expanding local support systems such as tech hubs and training programs would help bridge gaps in digital skills and access.

Homework Hack

import pandas as pd

# Load and clean the data
data = pd.read_csv("internet_users.csv").drop(columns=['Notes', 'Year.2', 'Users (CIA)', 'Rate (ITU)', 'Year.1'])  # Drop extra columns
data_cleaned = data.dropna()  # Drop rows with NaN (blank) values

# Extract necessary columns
y = data_cleaned['Rate (WB)']  # Percentage of the population using the internet from World Bank data
name = data_cleaned['Location']  # Country name from the data

# Loop through the data and print the results using iloc to avoid KeyError
for i in range(len(data_cleaned)):
    country = name.iloc[i]  # Use iloc for integer indexing
    internet_access = y.iloc[i]  # Use iloc for integer indexing
    
    if internet_access > 70:
        status = "doing great"
    else:
        status = "needs improvement"
    
    print(f"{country}: {internet_access}%: {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
Cayman 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

What I did

I added a loop that iterates through the cleaned dataset to evaluate each country’s internet access percentage. For each country, I used the range() function to loop through the rows and accessed the ‘Rate (WB)’ and ‘Location’ columns. The if-else condition checks if the internet access percentage is above 70%, and depending on the result, prints the country name, internet access percentage, and a message indicating either “doing great” or “needs improvement.”