Is python used in gcp github

Enid 48 Published: 10/09/2024

Is python used in gcp github

I'm happy to help!

Python is widely used in Google Cloud Platform (GCP) and GitHub. In fact, Python is one of the most popular programming languages used in GCP and GitHub.

In GCP, you can use Python as a scripting language to automate tasks, create custom functions for BigQuery, and even build machine learning models using TensorFlow or scikit-learn on Google Cloud AI Platform. You can also use Python with other GCP services like Cloud Storage, Cloud Functions, and Cloud Dataflow.

For example, you can use the Google Cloud Client Library for Python to interact with various GCP services, such as creating datasets in BigQuery or uploading data to Cloud Storage. You can also use popular libraries like pandas, numpy, and scikit-learn to perform data analysis, machine learning, and visualization tasks.

In GitHub, you can use Python to create scripts that automate repetitive tasks, such as updating repositories, sending notifications, or performing code analysis. You can also use Python to create custom GitHub Actions, which are workflows that automate tasks during software development, testing, or deployment processes.

For instance, you can use the PyGitHub library to interact with the GitHub API and perform tasks like creating pull requests, issuing commits, or updating repository contents. You can also use other libraries like requests or urllib3 to send HTTP requests and interact with GitHub's RESTful API.

Furthermore, many popular open-source projects on GitHub have Python codebases, which you can explore, contribute to, or even fork to create your own custom versions. Some examples include scikit-learn, TensorFlow, pandas, and numpy.

In conclusion, Python is a versatile language that is widely used in both Google Cloud Platform (GCP) and GitHub. Whether you're automating tasks, building machine learning models, or performing data analysis, Python is an excellent choice for many GCP and GitHub use cases.

GCP Python examples

I'd be happy to provide GCP Python examples for you! Here are a few:

Example 1: Creating a Cloud Storage Bucket

Here's an example of how to create a Cloud Storage bucket using the Google Cloud Client Library for Python:

from google.cloud import storage
Create a client object

client = storage.Client()

Create a new bucket

bucket = client.create_bucket("my-new-bucket", location="US")


print(f"Created bucket {bucket.name} in {bucket.location}")

Example 2: Running a Cloud Function

Here's an example of how to deploy and invoke a Cloud Function using the Google Cloud Client Library for Python:

from google.cloud import functions
Create a client object

client = functions.Client()

Deploy the function

function = client.deploy("my-function", runtime="python37",


source={"target": "gs://my-source-bucket/my-source-file.py"})


print(f"Deployed function {function.name}")

Invoke the function

response = client.invoke(function, {"input_data": "Hello, World!"})


print(response)

Example 3: Using BigQuery

Here's an example of how to create a BigQuery dataset and run a query using the Google Cloud Client Library for Python:

from google.cloud import bigquery
Create a client object

client = bigquery.Client()

Create a new dataset

dataset = client.create_dataset("my-new-dataset", location="US")


print(f"Created dataset {dataset.dataset_id} in {dataset.location}")

Run a query

job_config = bigquery.QueryJobConfig(query_params={"timeoutMs": 1000})


query_job = client.query_query(


"SELECT * FROM my-table WHERE column_name = 'value' LIMIT 10",


job_config


)


print(f"Query results: {list(query_job.results())}")

Example 4: Using Cloud Vision API

Here's an example of how to use the Cloud Vision API to analyze an image and detect objects using the Google Cloud Client Library for Python:

from google.cloud import vision
Create a client object

client = vision.ImageAnnotatorClient()

Load the image file

image_file = "path/to/image.jpg"


with open(image_file, "rb") as f:


image_data = f.read()

Annotate the image

response = client.annotate_image({"image": {"source": image_data}})


print(f"Detected objects: {', '.join([r.description for r in response.full_text_annotations])}")

These are just a few examples of what you can do with GCP and Python. I hope this helps! Let me know if you have any questions or need further assistance.

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