eazyetl

Introduction

eazyetl is a lightweight, beginner-friendly, and modular Python package for building end-to-end ETL (Extract, Transform, Load) pipelines. It provides intuitive classes and methods for working with data from various sources like APIs, CSV/JSON files, and databases, and helps you clean, transform, and load that data with ease.

Key changes.

In version 0.1.6, two new methods have been added to the Extract() class.

• Extract.read_db_via_url and
• Extract.read_db_via_params

Extract.read_db_via_url reads a SQL database e.g. MySQL or PostgreSQL via a database URI. This is easier and cleaner to use as compared to Extract.read_db_via_params which only reads a PostgreSQL database and entering those database values seems quite cumbersome for the user, but it is still included for users who might prefer this to using URIs.

i. Extract.read_db_via_url parameters:

• url datatype, str. The database URI, required parameter
• table_name datatype, str. The table name, required parameter
• columns datatype, list. A list of columns from the table
• parse_dates datatype, list or dict. This parameter tells our function which columns to parse as datetime objects.

ii. Extract.read_db_via_params parameters:

• database datatype str. The database name
• query datatype str. The SQL query to be executed when running the method. Example: 'SELECT * FROM employees'
• user datatype str. The user's username
• password datatype str. The user's password
• host datatype str. The database's host, default localhost
• port datatype str. The database's port number, default 5432

Installation

Install the package from TestPyPI:

pip install eazyetl

Features

• 📦 Extract from CSV, JSON, APIs, and PostgreSQL

• 🧹 Transform using common operations like dropna, replace, explode, to_datetime, and rename

• 📂 Load into CSV, JSON, Excel, PostgreSQL databases

• ☁️ Modular, static-method design (no complex setup required)

• 🐍 Designed with Pandas and SQLAlchemy for powerful data handling

Usage

a. Import the eazyetl library

from eazyetl import Extract, Transform, Load

b. Extract data from various sources using the Extract() methods

NOTE: The Extract.read_db() will include a database URL parameter to connect to databases more seamlessly rather than entering credentials which is more tiring. This will be available in version 0.2.0

NOTE: Version 0.2.0 will also contain a Extract.read_bucket() method which will enable users to read data from Amazon Web Services (AWS) Simple Storage Services (S3) buckets.

df = Extract.read_csv("data/data.csv")
api_data = Extract.read_api(url= 'https://fantasypremierleague.com/users/data') # not a real URL
db_data = Extract.read_db(database='employees', user='postgres', password='postgressuperuser', host='localhost', port='5432')

c. Transform data

df = Transform.drop_na(df, columns=["name", "price"])
df = Transform.to_datetime(df, "release_date")
df = Transform.rename(df, columns={"old_name": "new_name"})

d. Load data

Load.load_csv(df, "cleaned_data.csv", overwrite=True)
Load.load_to_excel(df, 'weather_data.xlsx', overwrite=False)
Load.load_to_db(df, name="salaries", url="postgresql://user:pass@localhost:5432/mydb")

Documentation

1. Extract

Method	Description
read_csv(filepath)	Load data from CSV
read_json(filepath)	Load data from JSON
read_api(url)	Load JSON data from an API
read_db_via_params(query, user, password, host, port, database)	Load data from PostgreSQL database
read_db_via_url(url, table_name, columns=None, parse_dates=None)	Load data from database URI

2. Transform

Method	Description
drop_na(data, columns=None, drop='index', inplace=False, how='any')	Drop missing values
replace(data, item_a, item_b, inplace=False)	Replace values
explode(data, columns)	Explode rows containing lists
changetype(data, dtype)	Change column or Series data type
to_datetime(data, column)	Convert column to datetime format
rename(data, columns=None, index=None, inplace=False)	Rename columns or index

3. Load

Method	Description
load_csv(data, filepath, overwrite=False)	Save data to CSV
load_json(data, filepath, overwrite=False)	Save data to JSON
load_to_excel(data, filepath, overwrite=False)	Save data to Excel (requires openpyxl)
load_to_db(data, name, url)	Save data to PostgreSQL table

Requirements.

These will be automatically installed by running the pip install eazyetl command.

• Python 3.7+

• pandas

• requests

• sqlalchemy

• psycopg2-binary

• openpyxl (for Excel file export)

Author

Name: Denzel 'deecodes' Kinyua

Data Engineer

GitHub: https://github.com/dkkinyua

Portfolio: https://denzel-kinyua.vercel.app

Email: denzelkinyua11@gmail.com

License

This project is licensed under the MIT License.

Contributions

Pull requests are welcome! If you'd like to suggest a feature or report a bug, open an issue on GitHub.