pdpcli 0.4.1

Description:

pdpcli 0.4.1

PdpCLI




Quick Links

Introduction
Installation
Tutorial

Basic Usage
Data Reader / Writer
Plugins



Introduction
PdpCLI is a pandas DataFrame processing CLI tool which enables you to build a pandas pipeline powered by pdpipe from a configuration file. You can also extend pipeline stages and data readers / writers by using your own python scripts.
Features

Process pandas DataFrame from CLI without wrting Python scripts
Support multiple configuration file formats: YAML, JSON, Jsonnet
Read / write data files in the following formats: CSV, TSV, JSON, JSONL, pickled DataFrame
Import / export data with multiple protocols: S3 / Databse (MySQL, Postgres, SQLite, ...) / HTTP(S)
Extensible pipeline and data readers / writers

Installation
Installing the library is simple using pip.
$ pip install "pdpcli[all]"

Tutorial
Basic Usage

Write a pipeline config file config.yml like below. The type fields under pipeline correspond to the snake-cased class names of the PdpipelineStages. Other fields such as stage and columns are the parameters of the __init__ methods of the corresponging classes. Internally, this configuration file is converted to Python objects by colt.

pipeline:
type: pipeline
stages:
drop_columns:
type: col_drop
columns:
- name
- job

encode:
type: one_hot_encode
columns: sex

tokenize:
type: tokenize_text
columns: content

vectorize:
type: tfidf_vectorize_token_lists
column: content
max_features: 10


Build a pipeline by training on train.csv. The following command generages a pickled pipeline file pipeline.pkl after training. If you specify a URL of file path, it will be automatically downloaded and cached.

$ pdp build config.yml pipeline.pkl --input-file https://github.com/altescy/pdpcli/raw/main/tests/fixture/data/train.csv


Apply the fitted pipeline to test.csv and get output of a processed file processed_test.jsonl by the following command. PdpCLI automatically detects the output file format based on the file name. In this example, the processed DataFrame will be exported as the JSON-Lines format.

$ pdp apply pipeline.pkl https://github.com/altescy/pdpcli/raw/main/tests/fixture/data/test.csv --output-file processed_test.jsonl


You can also directly run the pipeline from a config file without fitting pipeline.

$ pdp apply config.yml test.csv --output-file processed_test.jsonl


It is possible to override or add parameters by adding command line arguments:

pdp apply config.yml test.csv pipeline.stages.drop_columns.column=name

Data Reader / Writer
PdpCLI automatically detects a suitable data reader / writer based on a given file name.
If you need to use the other data reader / writer, add a reader or writer config to config.yml.
The following config is an exmaple to use SQL data reader.
SQL reader fetches records from the specified database and converts them into a pandas DataFrame.
reader:
type: sql
dsn: postgres://${env:POSTGRES_USER}:${env:POSTGRES_PASSWORD}@your.posgres.server/your_database

Config files are interpreted by OmegaConf, so ${env:...} is interpolated by environment variables.
Prepare yuor SQL file query.sql to fetch data from the database:
select * from your_table limit 1000

You can execute the pipeline with SQL data reader via:
$ POSTGRES_USER=user POSTGRES_PASSWORD=password pdp apply config.yml query.sql

Plugins
By using plugins, you can extend PdpCLI. This plugin feature enables you to use your own pipeline stages, data readers / writers and commands.
Add a new stage

Write your plugin script mypdp.py like below. Stage.register("<stage-name>") registers your pipeline stages, and you can specify these stages by writing type: <stage-name> in your config file.

import pdpcli

@pdpcli.Stage.register("print")
class PrintStage(pdpcli.Stage):
def _prec(self, df):
return True

def _transform(self, df, verbose):
print(df.to_string(index=False))
return df


Update config.yml to use your plugin.

pipeline:
type: pipeline
stages:
drop_columns:
...

print:
type: print

encode:
...


Execute command with --module mypdp and you can see the processed DataFrame after running drop_columns.

$ pdp apply config.yml test.csv --module mypdp

Add a new command
You can also add new commands not only stages.

Add the following script to mypdp.py. This greet command prints out a greeting message with your name.

@pdpcli.Subcommand.register(
name="greet",
description="say hello",
help="say hello",
)
class GreetCommand(pdpcli.Subcommand):
requires_plugins = False

def set_arguments(self):
self.parser.add_argument("--name", default="world")

def run(self, args):
print(f"Hello, {args.name}!")


To register this command, you need to create the .pdpcli_plugins file in which module names are listed for each line. Due to module importing order, the --module option is unavailable for command registration.

$ echo "mypdp" > .pdpcli_plugins


Run the following command and get a message like below. By using the .pdpcli_plugins file, it is is not needed to add the --module option to a command line for each execution.

$ pdp greet --name altescy
Hello, altescy!