Files and Real-World Data

Convert the loan text to int before adding.

from pathlib import Path
import csv

file_path = Path(__file__).resolve().parent.parent / 'datasets' / 'library.csv'
with file_path.open(newline='', encoding='utf-8') as handle:
    rows = list(csv.DictReader(handle))
total = sum(int(row['loans']) for row in rows)
print('Total loans:', total)

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max() accepts a key function.

from pathlib import Path
import csv

file_path = Path(__file__).resolve().parent.parent / 'datasets' / 'rainfall.csv'
with file_path.open(newline='', encoding='utf-8') as handle:
    rows = list(csv.DictReader(handle))
wettest = max(rows, key=lambda row: float(row['mm']))
print('Wettest:', wettest['day'])

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Filter rows after converting score to int.

from pathlib import Path
import csv

file_path = Path(__file__).resolve().parent.parent / 'datasets' / 'scores.csv'
with file_path.open(newline='', encoding='utf-8') as handle:
    rows = list(csv.DictReader(handle))
passing = [row['student'] for row in rows if int(row['score']) >= 60]
print('Passing:', ', '.join(passing))

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float() converts each price field.

from pathlib import Path
import csv

file_path = Path(__file__).resolve().parent.parent / 'datasets' / 'shops.csv'
with file_path.open(newline='', encoding='utf-8') as handle:
    rows = list(csv.DictReader(handle))
total = sum(float(row['price']) for row in rows)
print(f'Total: £{total:.2f}')

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Divide the sum by the number of rows.

from pathlib import Path
import csv

file_path = Path(__file__).resolve().parent.parent / 'datasets' / 'steps.csv'
with file_path.open(newline='', encoding='utf-8') as handle:
    rows = list(csv.DictReader(handle))
values = [int(row['steps']) for row in rows]
print('Average steps:', round(sum(values)/len(values), 1))

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Sum the count column.

from pathlib import Path
import csv

file_path = Path(__file__).resolve().parent.parent / 'datasets' / 'animals.csv'
with file_path.open(newline='', encoding='utf-8') as handle:
    rows = list(csv.DictReader(handle))
print('Animals:', sum(int(row['count']) for row in rows))

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Use max with a numeric key.

from pathlib import Path
import csv

file_path = Path(__file__).resolve().parent.parent / 'datasets' / 'energy.csv'
with file_path.open(newline='', encoding='utf-8') as handle:
    rows = list(csv.DictReader(handle))
room = max(rows, key=lambda row: float(row['kwh']))
print('Highest energy:', room['room'])

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Use min() to find the smallest minutes field.

from pathlib import Path
import csv

file_path = Path(__file__).resolve().parent.parent / 'datasets' / 'travel.csv'
with file_path.open(newline='', encoding='utf-8') as handle:
    rows = list(csv.DictReader(handle))
route = min(rows, key=lambda row: int(row['minutes']))
print('Fastest route:', route['route'])

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Compare row["genre"] to Computing.

from pathlib import Path
import csv

file_path = Path(__file__).resolve().parent.parent / 'datasets' / 'books.csv'
with file_path.open(newline='', encoding='utf-8') as handle:
    rows = list(csv.DictReader(handle))
count = sum(1 for row in rows if row['genre'] == 'Computing')
print('Computing books:', count)

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csv.DictWriter can write header and row data.

from pathlib import Path
import csv

file_path = Path(__file__).resolve().parent.parent / 'datasets' / 'scores.csv'
with file_path.open(newline='', encoding='utf-8') as handle:
    rows = list(csv.DictReader(handle))
from io import StringIO
output = StringIO()
writer = csv.DictWriter(output, fieldnames=['student','result'])
writer.writeheader()
for row in rows:
    writer.writerow({'student':row['student'], 'result':'Pass' if int(row['score'])>=60 else 'Retry'})
print(output.getvalue().strip())

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