High school computer science access by state

Suggested citation: Code.org (2025). 2025 State of AI + CS Education. Figures from the raw data, Most Recent Access Data tab; shares rounded to one decimal by us from the published counts. North Dakota’s row includes cybersecurity courses; under the national definition the report puts it at 53% for 2023 to 2024. The data come largely from state education agencies, with course catalogs and teacher surveys where a state does not report. We are not connected with Code.org, now called CodeAI.

Source: Code.org (2025). 2025 State of AI + CS Education, Most Recent Participation Data tab. The sheet notes that total student numbers come from the NCES Digest of Education Statistics, Table 203.04, Fall 2022, so the denominator is not always the same year as the enrollment count. Shares are rounded by us.

# offering, total: five rows typed from the access table (Code.org, 2025)
access = {
    "Alabama": (351, 382),
    "Florida": (463, 1192),
    "Maryland": (219, 219),
    "Minnesota": (275, 799),
    "Texas": (1036, 2160),
}

shares = {state: offering / total
          for state, (offering, total) in access.items()}

for state, share in sorted(shares.items(), key=lambda kv: kv[1], reverse=True):
    print(f"{state:<10} {share:6.1%}")

1. Store pairs, not percentages. Keeping offering and total lets you recompute and catch typing errors.
2. Use a dictionary comprehension to turn each pair into a share in one line.
3. Sort with a key. key=lambda kv: kv[1] sorts by the share, not the state name.
4. Format as a percentage. The :6.1% format multiplies by 100 and adds the percent sign.
5. Extend it. Add the year as a third value and print it, so no one compares 2023 to 2024 with 2024 to 2025 by accident.