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Utility library designed for Data Science, Data Engineering, and Python Development projects. It provides useful tools and functions to facilitate work in these fields. This project is intended for personal use but is available for anyone interested in exploring or contributing.

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juandaherrera/jds_tools

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JD Science Tools

Python Tag

Jinja2 gspread aiohttp Snowflake

Description

JD Science Tools is a utility library designed for Data Science, Data Engineering, and Python Development projects. It provides useful tools and functions to facilitate work in these fields. This project is intended for personal use but is available for anyone interested in exploring or contributing.

Table of Contents

Installation

To install JD Science Tools from PyPI, run the following command in your terminal:

pip install jds_tools

Ensure that you have pip installed on your system and that you are using Python 3.10 or higher.

Usage

JinjaHook

Imagine you have the following project structure:

.
├── project_folder
│ ├── queries
│ │ ├── my_query.sql
│ ├── main.py

Example SQL Template (my_query.sql)

Here is an example of a SQL template using Jinja:

SELECT 
    {% for column in columns %}
        {{ column }}{% if not loop.last %},{% endif %}
    {% endfor %}
FROM {{ schema }}.{{ table_name }}

Rendering the Query in main.py

To render your query with Jinja, you can use the JinjaHook from the jds_tools package as follows:

import os
from jds_tools.hooks import JinjaHook

# Initialize the JinjaHook with the directory containing your SQL templates
jinja = JinjaHook(os.path.join(os.getcwd(), "queries"))

# Define the parameters to pass into the template
params = dict(
    columns=["column1", "column2", "column3"],
    schema="my_schema",
    table_name="my_table"
)

# Render the SQL query with the parameters
query = jinja.render("my_query.sql", params)
print(query)

Resulting SQL Query

After rendering, you obtain the following SQL query:

SELECT
    colum1,
    column2,
    column3
FROM my_schema.my_table

SnowflakeHook

The following example demonstrates how to use the SnowflakeHook from the jds_tools package to interact with a Snowflake database. You will learn how to fetch data, upload data, and run multiple statement queries.

import os
from jds_tools.hooks import SnowflakeHook

# Initialize the SnowflakeHook with environment variables
snowflake_hook = SnowflakeHook(
    os.getenv('SNOWFLAKE_ACCOUNT'),
    os.getenv('SNOWFLAKE_USER'),
    os.getenv('SNOWFLAKE_PASSWORD'),
    os.getenv('SNOWFLAKE_WAREHOUSE'),
    os.getenv('SNOWFLAKE_DATABASE'),
)

# Fetch data from Snowflake
result = snowflake_hook.fetch_data("SELECT * FROM your_table")

# Uploading data
snowflake_hook.role = "your_role_with_write_permissions"
snowflake_hook.upload_data(result, "your_table", "your_schema", "replace")

# Running a multiple statement query
query = """
BEGIN;
USE DATABASE your_database;
USE SCHEMA your_schema;

CREATE OR REPLACE TABLE your_table AS
SELECT 1 AS TEST_COLUMN;
COMMIT;
"""
snowflake_hook.execute_statement(query)

GoogleSheetsHook

The GoogleSheetsHook class provides an interface for interacting with Google Sheets. Here are some examples of how to use it:

from jds_tools.hooks import GoogleSheetsHook

credentials_path = os.path.join(project_root, "your_google_key.json")
google_hook = GoogleSheetsHook("your_google_spreadsheet_id", credentials=credentials_path)

You can also insert the credentials as a string with a valid json or in a dictionary, like this:

credentials = {
    'type': 'service_account',
    'project_id': 'my_project',
    'private_key_id': 'my_private_key_id',
    'private_key': 'my_private_key',
    'client_email': 'my_client_email',
    'client_id': 'my_client_id',
    'auth_uri': 'https://accounts.google.com/o/oauth2/auth',
    'token_uri': 'https://oauth2.googleapis.com/token',
    'auth_provider_x509_cert_url': 'https://www.googleapis.com/oauth2/v1/certs',
    'client_x509_cert_url': 'https://www.googleapis.com/robot/v1/metadata/x509/my_client_email'
}
google_hook = GoogleSheetsHook('your_google_spreadsheet_id', credentials=credentials, credentials_type="variable")

And then use the Hook like this:

# Reading Data
g_data = google_hook.read("your_worksheet_name", return_df=True)

# Writing Data
google_hook.write("your_worksheet_name", g_data)

# Appending Data
google_hook.append("your_worksheet_name", g_data)

# Cleaning Data
google_hook.clear("your_worksheet_name", "A2:B99")

Asynct Requests

async_get

The async_get function sends asynchronous GET requests to multiple URLs and returns the responses.

import asyncio
from jds_tools.utils.async_requests import async_get

# Define the URLs to send GET requests to
urls = ["https://api.example.com/endpoint1", "https://api.example.com/endpoint2"]

# Define the headers to include in the requests
headers = {"Authorization": "Bearer token"}

# Send the requests and get the responses
responses = asyncio.run(async_get(urls, headers))

# Print the responses (dict with status, headers, text, json)
for response in responses:
    print(response)

async_post

The async_post function sends asynchronous POST requests to a given URL with multiple sets of data and headers.

import asyncio
from jds_tools.utils.async_requests import async_post

# Define the URL to send POST requests to
url = "https://api.example.com/endpoint"

# Define the data to be sent in the requests
data = [
    {"name": "John", "age": 30},
    {"name": "Jane", "age": 25}
]

# Define the headers to include in the requests
headers = {"Content-Type": "application/json"}

# Send the requests and get the responses
responses = asyncio.run(async_post(url, data, headers))

# Print the responses (dict with status, headers, text, json)
for response in responses:
    print(response["status"])
    print(response["json"])

Path Utils

add_project_root_to_sys_path

This function adds the project root directory to sys.path if it's not already there. If project_root is not provided, it will start from the current directory and search upwards for a directory containing any of the files in ROOT_FILES. If it doesn't find a directory containing any of those files within max_depth directories, it raises an exception. If recursive_search is False, it won't search upwards and will just use the current directory as the project root.

from jds_tools.utils.path_utils import add_project_root_to_sys_path

# Add the current directory's parent directory to sys.path
add_project_root_to_sys_path()

# If you know the project root and don't want to search recursively
add_project_root_to_sys_path(project_root='/path/to/your/project', recursive_search=False)

# If you want to limit the depth of the recursive search
add_project_root_to_sys_path(max_depth=3)

License

This project is licensed under the MIT License.

Contributing

Contributions are welcome! If you would like to contribute to this project, please fork the repository and submit a pull request. For major changes, please open an issue first to discuss what you would like to change.

Contact

If you have any questions, suggestions, or just want to connect, feel free to reach out to me via:

Platform Contact
Email juandaherreparra@gmail.com
LinkedIn Linkedin: Juan David Herrera

About

Utility library designed for Data Science, Data Engineering, and Python Development projects. It provides useful tools and functions to facilitate work in these fields. This project is intended for personal use but is available for anyone interested in exploring or contributing.

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