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Python Concepts for Data Science: Generators

A generator is a special type of python function that returns an iterator (generator) object. Contrary to the normal function, a generator uses the yield statement instead of return. However, functions and generators are completely different by syntax and semantic.

In this article, I will first depict the concept of generator with examples of how to apply it in data science, then I will give some advantages and finally some limitations of python generators.

1. Generator syntax and examples

a. Syntax

As can be shown on cover image, the syntax of a python generator is as follows:

  • Generator function

def gen_name(arguments): 
        List of instructions  
       yield result
  • Generator expression

(exp for exp in collection if condition)

b. Examples

Throughout this article, we will be working with a dummy dataframe named color that contains 8 color names associated with their matplotlib and hexadecimal or html codes.

import pandas as pd

data = {'color_name': ['blue', 'red', 'black', 'white', 'magenta', 'green', 'cyan', 'yellow'],
       'plt_code' : ['b', 'r', 'k', 'w', 'm', 'g', 'c', 'y'],
        'hex_code' : ['#0000ff', '#ff0000', '#000000', '#ffffff', '#ff00ff', '#008000', '#00ffff', '#ffff00']}

color = pd.DataFrame(data)

To make sure our dataframe has the expected shape and organization, let's print its first few rows.


# Output

To illustrate the concept of generator function, let's create a generator read_df that takes two positional arguments namely:

df, a Pandas Dataframe and column, the name of one of its columns and yields a generator object of all the elements of that column in the dataframe.

def read_df(df, column):
    """Returns a generator of column elements"""
    for elt in df[column]:
        yield elt

It is very important to notice how the yield statement is under a for loop. This is a special characteristic of python generator functions. Unlike conventional functions, the yield statement can be used more than once. Another special, the most relevant characteristic of generators is that they don't store a list of objects into the memory. When instantiated, the elements of a generator are accessible one at a time using the function next. The current element is stored and calling next again retrieves the following one up to the last, after what a StopIteration error is raised.

In the example below, we create a generator object name_gen that holds all the values of the name column of color dataframe.

name_gen = read_df(df, 'color_name')

Now let's check the type of name_gen and print out its 3 first values. To do so, we need to call next three times.

print(next(name_gen))  # First value
print(next(name_gen))   # Second value
print(next(name_gen))    # Third value
# Output
<class 'generator'>

The same iterator can be created using a generator expression which is a simpler way than generator function.

name_gen1 = (item for item in color['color_name'])
print(next(name_gen1))  # First value
print(next(name_gen1))   # Second value
print(next(name_gen1))    # Third value
# Output
<class 'generator'>

2. Advantages of python generators

The python generators provide programmers with the followings advantages, over other concepts used for the same task.

a. Easy Implementation

An iterator class can be used to create iterators, just like generators do. However, compared to the iterator class, the implementation of a generator is simple, easy and require less lines of code. Let's illustrate this by creating an iterator class Timestwo and a generator function timestwo that perform the same operation: multiply a given stream by two in the item level.

# iterator class
class Timestwo:
    def __init__(self, n_iter=0):
        self.n = 0
        self.n_iter = n_iter

    def __iter__(self):
        return self

    def __next__(self):
        if self.n > self.n_iter:
            raise StopIteration

        result = self.n * 2
        self.n += 1
        return result
# Generator function
def timestwo(n_iter=0):
    n = 0
    while n < n_iter:
        yield n * 2
        n += 1

The difference in complexity between the two code snippets is quite clear. Defining the iterator class requires much more code than defining the generator function. Besides, the generator code is highly legible and understandable than its Iterator class counterpart.

b. Memory efficiency

If we want a function to draw all the values from a certain of a given dataframe, the only way to achieve this is by storing these values as list or another dataframe. For the color dataframe above, this won't be a problem since every column consists of only 8 values. For larger dataframes with hundreds of thousands, or even millions of values, this can become very memory consuming. In this case a generator would be the most relevant solution.

c. Infinite stream representation

Related to memory efficiency, a generator is the most suited way to represent infinite stream of data. In fact, infinite stream of data cannot hold into memory, thus need to be treated one after another or by chunks.

For example, the mult_three generator function can theoretically generate all the integers that are divisible by three.

def mult_three():
    n = 0
    while True:
        yield n
        n += 3
by_three = mult_three()
# Output

Contrary to the conventional function, a call to mult_three won't freeze the program.

3. When to avoid using python generators

Like any other python concept, generators are very useful but not perfect in that they aren't always the best fit in all the situations involving stream of data. Below are few cases where generators should be avoided.

  • You need to access data multiple times, a generator allows to access data only once;

  • You need to randomly draw an element from a stream of data;

  • You need to join strings. A list is the best fit because this process requires two passes over data.


Generators are one of the most powerful python concepts when it comes to deal with large stream of data that need to be accessed one item at a time. This make generators very useful when the stream is particularly long or can't fit into memory. However if we need to access each data point more than once, conventional lists become more suited.

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