Online coding and AI classes in the Pasco region

ENAHO results in INEI, Compendio Estadístico Perú 2026, volume 1, tables 11.5 (computers), 11.6 (internet in the home) and 11.13 (internet use). Commas in INEI's decimals changed to points.

import csv
import re

with open("atractivos-pasco.csv", newline="", encoding="latin-1") as f:
    rows = list(csv.DictReader(f))

found = 0
for row in rows:
    text = row["DESCRIPCION_DEL_ATRACTIVO"]
    match = re.search(r"(\d+) km", text)
    if match:
        found += 1
        print(row["NOMBRE_DEL_ATRACTIVO_TURISTICO"].strip()[:28], match.group(1))
print("Attractions with a distance:", found, "of", len(rows))

Version 1 looks as if it works. Eleven attractions report a distance, and the numbers seem sensible. But check one by hand. The replica of Daniel Alcides Carrión's house is described as "a 1.8 km de la ciudad de Cerro de Pasco", and the program printed 8. The pattern \d+ means "one or more digits", so it skipped the 1 and the full stop and matched only the 8 right before " km". The second trap is quieter: the numbers do not all start from the same place. The two waterfalls are measured from Villa Rica, Pozuzo and Villa Rica from Oxapampa, everything else from Cerro de Pasco. A list of bare numbers mixes them up.

import csv
import re

PATTERN = r"(\d+(?:\.\d+)?) km[^(]*?(?:ciudad|poblado) de ([^(]+?) \("

with open("atractivos-pasco.csv", newline="", encoding="latin-1") as f:
    rows = list(csv.DictReader(f))

by_start = {}
for row in rows:
    match = re.search(PATTERN, row["DESCRIPCION_DEL_ATRACTIVO"])
    if match:
        km = float(match.group(1))
        start = match.group(2)
        by_start.setdefault(start, []).append(km)

for start, distances in sorted(by_start.items()):
    print(start, sorted(distances))
print("Distances read:", sum(len(d) for d in by_start.values()))

The new pattern has two groups. (\d+(?:\.\d+)?) takes digits with an optional decimal part, so 1.8 survives whole. (?:ciudad|poblado) de ([^(]+?) \( captures the place named after "ciudad de" or "poblado de", up to the bracket that opens the travel time. Grouping by that place gives three separate lists. The Huaguruncho snow peak, the furthest from Cerro de Pasco at 86 km, and Pozuzo, 87 km from Oxapampa, are now plainly measured from different towns.

Data: Municipalidad Provincial de Pasco, Lista de Atractivos Turísticos de la Provincia de Pasco, ODC-By. The extraction and grouping are ours. Fifteen of the 26 descriptions contain no distance in this form and are left out. One description gives Villa Rica's altitude as 1480 msnm, while INEI's 2017 list puts the populated centre at 1,551 metres: two sources, two points, both printed as published.

1. Under 8, with a map. Draw three towns and a few places around each. Say how far each place is "from" which town, and why you must say which.
2. Blocks, around 8 to 10. Give a sprite a list of sentences. Can it find the word "km" and say the word just before it?
3. First Python, around 10 to 12. Use text.split() to find the word before "km" in each description. Does it handle 1.8?
4. Ages 13 and over. Extend Version 2 to read the travel time in brackets, in minutes, and work out an average speed for each trip.
5. Going further. Pull every altitude written as "msnm" and compare it with INEI's centros poblados list. Where do the two disagree, and by how much?

Our suggestions. Grade is set by age on 31 March, so one class can hold children almost a year apart in age.

University students can read coding courses for university students in Peru, and anyone weighing a first language can see our Python course for Peru. The full set of Peru guides is on our Peru page.