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How to Read a CSV File from Amazon S3 Using Python (With Headers and Rows Displayed)

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  Introduction If you’re working with cloud data, especially on AWS, chances are you’ll encounter data stored in CSV files inside an Amazon S3 bucket . Whether you're building a data pipeline or a quick analysis tool, reading data directly from S3 in Python is a fast, reliable, and scalable way to get started. In this blog post, we’ll walk through: Setting up access to S3 Reading a CSV file using Python and Boto3 Displaying headers and rows Tips to handle larger datasets Let’s jump in! What You’ll Need An AWS account An S3 bucket with a CSV file uploaded AWS credentials (access key and secret key) Python 3.x installed boto3 and pandas libraries installed (you can install them via pip) pip install boto3 pandas Step-by-Step: Read CSV from S3 Let’s say your S3 bucket is named my-data-bucket , and your CSV file is sample-data/employees.csv . ✅ Step 1: Import Required Libraries import boto3 import pandas as pd from io import StringIO boto3 is...

Sets Vs Lists Python Programmer Tips

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Sets are only useful when trying to ensure unique items are preserved. Before sets were available, it was common to process items and check if they exist in a list (or dictionary) before adding them. List example Here unique is an empty list. Every time I compare with this list, and if it is not duplicated then the input item will append to the unique list.  >>> unique = []  >>> for name in ['srini', 'srini', 'rao', 'srini']:  ... if name not in unique:  ... unique.append(name)  ... >>> unique ['srini', 'rao'] There is no need to do this when using sets. Instead of appending you add to a set: Set example >>> for name in ['srini', 'srini', 'rao', 'srini']: ... unique.add(name)  ...  >>> unique {'srini', 'rao'} Just like tuples and lists, interacting with sets have some differences on how to access their items. You can't index them like lists an...