coding-mentor-agent/kb/python-course-kb-practical-python/wiki/exercises/6-10-making-more-pipeline-components.md

id, source_exercise_id, title, section, source_path, source_repo, source_commit, student_visible_solution, has_private_solution, skip

id	source_exercise_id	title	section	source_path	source_repo	source_commit	student_visible_solution	has_private_solution	skip
practical-python-6.10	6.10	Making more pipeline components	6.3 Producers, Consumers and Pipelines	06_Generators/03_Producers_consumers.md	https://github.com/dabeaz-course/practical-python	93dca856b41c61a0a0f85ae334116e4c125629ea	false	false	false

Exercise 6.10: Making more pipeline components

Source: Practical Python Programming, 06_Generators/03_Producers_consumers.md.

Exercise 6.10: Making more pipeline components

Let's extend the whole idea into a larger pipeline. In a separate file ticker.py, start by creating a function that reads a CSV file as you did above:

# ticker.py

from follow import follow
import csv

def parse_stock_data(lines):
    rows = csv.reader(lines)
    return rows

if __name__ == '__main__':
    lines = follow('Data/stocklog.csv')
    rows = parse_stock_data(lines)
    for row in rows:
        print(row)

Write a new function that selects specific columns:

# ticker.py
...
def select_columns(rows, indices):
    for row in rows:
        yield [row[index] for index in indices]
...
def parse_stock_data(lines):
    rows = csv.reader(lines)
    rows = select_columns(rows, [0, 1, 4])
    return rows

Run your program again. You should see output narrowed down like this:

['BA', '98.35', '0.16']
['AA', '39.63', '-0.03']
['XOM', '82.45','-0.23']
['PG', '62.95', '-0.12']
...

Write generator functions that convert data types and build dictionaries. For example:

# ticker.py
...

def convert_types(rows, types):
    for row in rows:
        yield [func(val) for func, val in zip(types, row)]

def make_dicts(rows, headers):
    for row in rows:
        yield dict(zip(headers, row))
...
def parse_stock_data(lines):
    rows = csv.reader(lines)
    rows = select_columns(rows, [0, 1, 4])
    rows = convert_types(rows, [str, float, float])
    rows = make_dicts(rows, ['name', 'price', 'change'])
    return rows
...

Run your program again. You should now a stream of dictionaries like this:

{ 'name':'BA', 'price':98.35, 'change':0.16 }
{ 'name':'AA', 'price':39.63, 'change':-0.03 }
{ 'name':'XOM', 'price':82.45, 'change': -0.23 }
{ 'name':'PG', 'price':62.95, 'change':-0.12 }
...

关联来源

• summaries/03_Producers_consumers