Exercises in .NET with Andras Nemes

Python language basics 68: iterating over a dictionary with dictionary comprehension in Python

Introduction

In the previous post we looked at a Python language construct called comprehension. We saw that comprehensions are a very concise way of iterating over a collection, applying a function to each element and adding the resulting element to a new collection. All that is declared in a single comprehension statement.

In this post we’ll look at dictionary comprehensions.

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Python language basics 67: iterating over a list with list comprehension

Introduction

In the previous post we looked at a practical example using the yield keyword in Python. The function distributed an integer across a number of groups as equally as possible.

In this post we’ll look at an interesting language construct called comprehension. A comprehension in Python is a form of iteration which provides a concise and expressive way of iterating over a collection. In particular we’ll consider list comprehensions, but keep in mind that comprehensions can be applied to any iterable object, such as sets, tuples or generator functions. The delimiting element, such as curly braces for sets and square brackets for lists will determine the type of object returned by the comprehension.

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Python language basics 66: a practical example of a yield generator in Python

Introduction

In the previous post we continued our investigation of yield generators in Python. In particular we looked at how a yield function could remember the state of its internal variables. We saw how code execution jumps in and out of a yield function as it’s being enumerated.

At this point you may think that yield functions are interesting but what can they be used for? You’ll find a lot of examples on the internet. In this post we’ll look at the following scenario:

Say you’d like to divide an integer, e.g. 20, into equal parts of 3 and distribute any remainder equally across the groups. The result of such an operation would be the following 3 integers:

7,7,6

20 can be divided into 3 equal parts of 6 and we have a remainder of 20 – 6 * 3 = 2. 2 is then added as 1 and 1 to the first two groups of 6. The result is a more or less equal distribution of the start integer.

The following function will perform just that:

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Python language basics 65: keeping state in yield generators

Introduction

In the previous post we looked at a basic example of using the yield keyword in Python. We saw that yield generator functions are essentially collections that return a certain number of elements when they are enumerated. The elements to be returned – yielded – are not loaded into memory before they are required in a loop. We also said that generator functions can “remember” the state of their variables.

We’ll see an example of what this means in code.

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