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How to Build CI/CD Pipeline: GitHub to AWS

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 Creating a CI/CD pipeline to deploy a project from GitHub to AWS can be done using various AWS services like AWS CodePipeline, AWS CodeBuild, and optionally AWS CodeDeploy or Amazon ECS for application deployment. Below is a high-level guide on how to set up a basic GitHub to AWS pipeline: Prerequisites AWS Account : Ensure access to the AWS account with the necessary permissions. GitHub Repository : Have your application code hosted on GitHub. IAM Roles : Create necessary IAM roles with permissions to interact with AWS services (e.g., CodePipeline, CodeBuild, S3, ECS, etc.). AWS CLI : Install and configure the AWS CLI for easier management of services. Step 1: Create an S3 Bucket for Artifacts AWS CodePipeline requires an S3 bucket to store artifacts (builds, deployments, etc.). Go to the S3 service in the AWS Management Console. Create a new bucket, ensuring it has a unique name. Note the bucket name for later use. Step 2: Set Up AWS CodeBuild CodeBuild will handle the build proces
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How to Read CSV file Data in Python

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Here is a way to read CSV files in Python pandas. The packages you need to import are numpy and pandas. On the flip side, for Text files, you don't need to import these special libraries since python by default support it.



pandas read_csv


Python pandas read_csv


>>> import numpy as np
>>> import pandas as pd


To see how pandas handle this kind of data, we'll create a small CSV file in the working directory as ch05_01.csv.

white, red, blue, green, animal
1,5,2,3,cat 
2,7,8,5,dog 
3,3,6,7,horse 
2,2,8,3,duck 
4,4,2,1,mouse


Since this file is comma-delimited, you can use the read_csv() function to read its content and convert it to a dataframe object.
>>> csvframe = pd.read_csv('ch05_01.csv')
>>> csvframe
   white  red  blue  green animal
0      1    5     2      3    cat
1      2    7     8      5    dog
2      3    3     6      7  horse
3      2    2     8      3   duck
4      4    4     2      1  mouse


Python reading text files


Since python supports text files, you don't need to import NumPy and Pandas. The syntax is a little different. 

Using the Open method, here file is opened with read mode. In the place file name, it has given; the full path of the file. The Print method displays contents. Here read method is used to read the file.

# Open our file in read mode 
f = open("data/flatland01.txt", mode="r") 
# Read and display the text file 
print(f.read())
# Close our file resource 
f.close()

Finally, working with CSV and Text files knowing is helpful for interviews.


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