README.md

pyspark-bucketmap

pyspark-bucketmap is a tiny module for pyspark which allows you to bucketize DataFrame rows and map their values easily.

Install

pip install pyspark-bucketmap

Usage

from pyspark.sql import Row

people = spark.createDataFrame(
    [
        Row(age=12, name="Damian"),
        Row(age=15, name="Jake"),
        Row(age=18, name="Dominic"),
        Row(age=20, name="John"),
        Row(age=27, name="Jerry"),
        Row(age=101, name="Jerry's Grandpa"),
    ]
)
people

Now, what we would like to do, is map each person's age to an age category.

age range	life phase
0 to 12	Child
12 to 18	Teenager
18 to 25	Young adulthood
25 to 70	Adult
70 and beyond	Elderly

We can use pyspark-bucketmap for this. First, define the splits and mappings:

from typing import List

splits: List[float] = [-float("inf"), 0, 12, 18, 25, 70, float("inf")]
mapping: Dict[int, Column] = {
    0: lit("Not yet born"),
    1: lit("Child"),
    2: lit("Teenager"),
    3: lit("Young adulthood"),
    4: lit("Adult"),
    5: lit("Elderly"),
}

Then, apply BucketMap.transform(df):

from pyspark_bucketmap import BucketMap
from typing import List, Dict

bucket_mapper = BucketMap(
    splits=splits, mapping=mapping, inputCol="age", outputCol="phase"
)
phases_actual: DataFrame = bucket_mapper.transform(people).select("name", "phase")
phases_actual.show()

name	phase
Damian	Teenager
Jake	Teenager
Dominic	Young adulthood
John	Young adulthood
Jerry	Adult
Jerry's Grandpa	Elderly

Success!

✨

API

Module pyspark_bucketmap:

from pyspark.ml.feature import Bucketizer
from pyspark.sql import DataFrame
from pyspark.sql.column import Column

class BucketMap(Bucketizer):
    mapping: Dict[int, Column]

    def __init__(self, mapping: Dict[int, Column], *args, **kwargs):
        ...

    def transform(self, dataset: DataFrame, params: Optional[Any] = None) -> DataFrame:
        ...

Contributing

Under the hood, uses a combination of pyspark's Bucketizer and pyspark.sql.functions.create_map. The code is 42 lines and exists 1 in file: pyspark_bucketmap.py. To contribute, follow your preferred setup option below.

Option A: using a Devcontainer (VSCode only)

If you happen to use VSCode as your editor, you can open pyspark-bucketmap in a Devcontainer. Devcontainers allow you to develop inside a Docker container - which means all dependencies and packages are automatically set up for you. First, make sure you have the Remote Development extension installed.

Then, you can do two things.

1. Click the following button:

   

2. Or, clone and open up the repo in VSCode:

   git clone https://github.com/dunnkers/pyspark-bucketmap.git
   code pyspark-bucketmap

   (for this to work, make sure you activated VSCode's code CLI)

   Then, you should see the following notification:

   

Now you should have a fully working dev environment working 🙌🏻. You can run tests, debug code, etcetera. All dependencies are automatically installed for you.

🙌🏻

Option B: installing the dependencies manually

Clone the repo and install the deps:

git clone https://github.com/dunnkers/pyspark-bucketmap.git
cd pyspark-bucketmap
pip install -r .devcontainer/requirements.txt
pip install -r .devcontainer/requirements-dev.txt
pip install .

Make sure you also have the following installed:

• Python 3.9
• OpenJDK version 11

Now, you should be able to run tests 🧪:

pytest .

🙌🏻

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Created by Jeroen Overschie © 2022.