1 Data Structures

When I work in Python, I generally focus on three core (fundamental) data structures.

• Lists: prime_numbers = [1, 2, 3, 5, 7, 11, 13, 17, 19]
• Dictionaries: favourite_colors = {"Thomas": "Blue", "Jessica": "Purple"}
  • collections.defaultdict
  • collections.Counter
• Sets: some_colors = {"Blue", "Red", "Green", "Cyan", "Teal"}

If we want to map these to (modern) C++ and Java… we end up with…

1.1 Lists & List Comprehensions

1.2 Modern C++ and std::transform

Modern C++11 and newer provide the std::transform method. Combined with lambda functions we can take the original C++ code… and rewrite it as

Example 5: Word Count - C++ `std::transform`

using std::string;
using std::vector;


int main(int argc, char** argv)
{
    vector<string> some_terms {"Hello", "world", "with", "for", "while", "int"};
    vector<int> term_lengths;

    std::transform(some_terms.begin(), some_terms.end(), std::back_inserter(term_lengths),
                   [](const string& t) -> int {
                       return t.size();
                   });

    return 0;
}

Java has the java.util.stream package, which provides similar functionality to Python comprehensions and C++ std::transform. However, in Java, we would end up dealing with the Integer wrapper class if we wanted to use a non-array data structure.

Example 6: Word Count - Java Streams

import java.util.Arrays;
import java.util.List;

public class IntStreamDemo
{
    public static void main(String... args)
    {
        List<String> some_terms = Arrays.asList("Hello", "world", "with",
                                                "for", "while", "int");

        int[] term_lengths = some_terms.stream()
                           .mapToInt(s -> s.length())
                           .toArray();
    }
}

The Python implementation is the most succinct, approachable, and readable.

2 Context Managers

Python provides the with statement (construct). This allows the setup and teardown involved in using resources (e.g., files, sockets, and database connections) to handled elsewhere.

This has two main benefits:

1. There is less boilerplate code.
2. It is impossible to forget to close/deallocate a resource.

To write to a file, one might write:

Example 7: Python File IO - Basic

text_file = open("some_file.txt", "w")

for number in range(1, 100):
    text_file.write(f"{number}\n")

Did you notice the missing fclose(text_file)? With one small with the file close operation will be handled automatically.

Example 8: Python File IO - Using `with`

with open("some_file.txt", "w") as text_file:
    for number in range(1, 100):
        text_file.write(f"{number}\n")

This also works for other types of files–including compressed files.

Example 9: Python File IO - Using `with` and `gzip`

import gzip

with gzip.open("some_file.txt.gz", "wt") as text_file:
    for number in range(1, 100):
        text_file.write(f"{number}\n")

3 Python Includes Batteries