How To

This section contains examples of common operations in RxSci and Maki Nage. You can find the associated code for most of them here.

Import and Export Data

Read a csv file

You can work directly on CSV files (vs using Kafka as a source and sink), via the load_from_csv factory operator.

First import the csv module that contains all csv operators:

import rxsci.container.csv as csv

Then you must declare the schema of the CSV file. Below is the schema of the iris dataset that you can retrieve from kaggle.

iris_parser = csv.create_line_parser(
        ('id', 'int'),
        ('sepal_length_cm', 'float'),
        ('sepal_width_cm', 'float'),
        ('petal_length_cm', 'float'),
        ('petal_width_cm', 'float'),
        ('species', 'str'),

The create_line_parser operator supports options to customize the parser, such as the text encoding, column separator, and default values. See the documentation for more information.

iris_data = csv.load_from_file('./Iris.csv', iris_parser)

Write a csv file

The dump_to_file operator writes each input item to a row of a CSV file. The input items must be namedtuples. So first ensure that your data is structured as a namedtuple:

IrisFeature = namedtuple('IrisFeature', ['id', 'species', 'sepal_ratio', 'petal_ratio'])

iris_features = iris_data.pipe( i: IrisFeature(, species=i.species,
        sepal_ratio=i.sepal_length_cm / i.sepal_width_cm,
        petal_ratio=i.petal_length_cm / i.petal_width_cm

The dump_to_file operator uses the fields of the namedtuple to infer the columns names of the CSV file:

    csv.dump_to_file('iris_features.csv', encoding='utf-8'),

Extend Maki Nage

Create an operator by composition

The simplest way to create a new operator is by composing other existing operators. Let’s consider these three operations done on some text input: i: i.replace("-", " "))
rs.ops.filter(lambda i: 'bill' not in i) i: i.capitalize())

The natural way to use them is by chaining them in a pipe:

data.pipe( i: i.replace("-", " "))
    rs.ops.filter(lambda i: 'bill' not in i) i: i.capitalize())

But as more and more transforms are added, you can end up with a very long pipe. You can easily improve the readability and reuse some operations by grouping operators. For example, the previous three operators can be grouped as a custom operator:

def cleanup_text():
    return rx.pipe( i: i.replace("-", " ")),
        rs.ops.filter(lambda i: 'bill' not in i), i: i.capitalize()),

The function cleanup_text is an operator that you can use in a pipe:

data = [
    'bill is fast',
    'lorem ipsum'

The quick brown fox
Lorem ipsum

Create a stateful operator by composition

Stateful operators are more complex to implement because they need to update a state. Hopefully, In many cases, you can create new operators by combining three base operators: scan, filter, and map:

  • The scan operator updates the state.

  • The filter operator controls when items must be emitted.

  • The map operator emits the items from the state.

Let’s consider the following need: Sum all items up to some threshold. An item must be emitted each time the sum would cross the threshold. Then the sum process starts again:


The state logic can be implemented with the following function:

def _sum_split(acc, i):
        if acc[0] + i > threshold:
            return (i, acc[0])
        return (acc[0]+i, None)

Here acc contains the state. It is a tuple where:

  • The first field is the current sum

  • The second field is the item to emit or None if nothing must be emitted.

The full implementation of the operator simply consists in combining scan, filter, and map in a wrapper function:

def sum_split(threshold):
    def _sum_split(acc, i):
        if acc[0] + i > threshold:
            return (i, acc[0])
        return (acc[0]+i, None)

    return rx.pipe(
        rs.ops.scan(_sum_split, seed=(0, None)),
        rs.ops.filter(lambda i: i[1] is not None), i: i[1]),

You can now use sum_split just as any builtin operator:

data = [1, 2, 3, 4, 1, 2, 6, 1]

3 3 5 2 6

Run a local Kafka server