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Chapter 13 | Advanced Iteration | Looping Like Crazy

Introduction

  • We can optimize loops with this 2 approaches.
    • better loop syntax.
    • better loop performance.
  • Python provides both of the above in terms of comprehension.

CSV

  • CSV can be read by just using the plain vanilla file read. Just that we have to put effort to manipulate the data.
  • Python provides an important module called csv which solves the issue of reading CSV file.
    • csv.reader() : reads 1 line at a time, separates the data based on ,
    • csv.DictReader() : Reads the first row as a key, and the subsequent rows as value of the keys.
  • Raw data can also be manipulated by using string's api and list apis.
  • split() : split is a string api, which splits a string based on a delimiter like ,, and splits into tuple.
  • strip() : removes the white spaces from a strings start and end, and returns a string.
  • We need few conversion, for date time and strings.
    • strptime() : Takes a string representation and changes it into time structure, based on the format.
    • strftime() : Converts a time structure into a string based on the format passed.
    • Few format specifiers are as below.
      • %H : shows hours, use 24 hrs clock.
      • %I : shows hours, use 12 hrs clock.
      • %M : shows the minutes.
      • %p : shows am or pm based on given time value.
  • .title() : converts a string into a title case.

Comprehension

  • Comprehension are a faster and efficient way to write for loops.
  • We can have these different type of comprehension.
    • List
    • Dict
    • Set

List Comprehensions

  • flight_times2 = [convert2ampm(ft) for ft in flights.keys()]
    • The right side creates a list comprehension,
    • The for loop is executed first, and each value of ft is passed to convert2ampm, and stored in list.
    • The above process is repeated for each value in the flights.keys()
  • The above list comprehension, can also be used to filter values.
    • flight_times2 = [convert2ampm(ft) for ft in flights.keys() if ft == '17:00']
      • This comprehension is similar, but the ft is first filtered with the if conditions.
      • Once passing that condition is then stored in the list.

Dictionary Comprehension

  • more_flights = {convert2ampm(k): v.title() for k, v in flights.items()}
    • The above is an example of dictionary comprehension.
    • The for loop is executed first, and the k and v from each iteration, is used to form a key value pair
    • convert2ampm(k): v.title()
      • The key value is store after conversion, the key is converted to string in am or pm format.
      • The value(v) is converted to title case and stored.
  • The dictionary comprehension, just like list comprehension can be filtered.
    • filtered_flights = {convert2ampm(k): v.title() for k, v in flights.items() if v == "FREEPORT"}
      • Everything happens like before, only the value of k and v is stored when v == "FREEPORT" condition is matched.

Set Comprehension

  • found2 = {v for v in vowels if v in message}
    • The above is an example of set comprehension, it is almost similar to Dictionary comprehension.
    • The main difference between the set and dict comprehension, is the absence : in set comprehension.

Tuple Comprehension or Generator

for i in (x ** 3 for x in [1, 2, 3, 4, 5]):
    print(i)
  • Though the above code looks to be using tuple comprehension, but actually the code within () creates an generator.
  • When we use list comprehension, all the intermediate data for the comprehensions are stored and then finally the combined data is returned.
  • This can cause issues of memory.
  • We can replace the same concept but surround it with () making it a generator.
  • A generator returns only 1 items at a time.
def gen_from_urls(urls: tuple) -> tuple:
    for resp in (requests.get(url) for url in urls):
        # yield returns only 1 items at a time.
        yield len(resp.content), resp.status_code, resp.url
  • When a functions executes a return statement, the function terminates.
  • When a functions sees a yield statement, it returns only the current value,

Bullet Points

  • Python has many different modules in built to work on data, open() is one, csv module is another.
  • Method chaining helps in multiple things done in a single line of code.
  • While chaining methods care should be taken, about the data structure returned by the function.
  • for loop can be reworked into a comprehension.
  • Comprehension can used to process existing list, dict, or sets.
  • There is nothing called tuple comprehension, as tuple are immutable.
  • Generator looks a lot like a tuple comprehension, and uses yield to generate data.

Reference