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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...
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How to Understand Pickling and Unpickling in Python

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Here are the Python pickling and unpickling best examples and the differences between these two.


pickling and unpickling in python




These you can use to serialize and deserialize the python data structures. The concept of writing the total state of an object to the file is called pickling, and to read a Total Object from the file is called unpickling.


Pickle and Unpickle

The process of writing the state of an object to the file (converting a class object into a byte stream) and storing it in the file is called pickling. It is also called object serialization.

The process of reading the state of an object from the file ( converting a byte stream back into a class object) is called unpickling. It is an inverse operation of pickling. It is also called object deserialization. The pickling and unpickling can implement by using a pickling module since binary files support byte streams. Pickling and unpickling should be possible using binary files.


Data types you can pickle

  1. Integers
  2. Booleans
  3. Complex numbers
  4. Floats
  5. Normal and Unicode strings
  6. Tuple
  7. List
  8. Set and dictionaries which contains pickling objects
  9. Classes and built-in functions can define at the top level of a module.

Functions you need


dump()


The above function performs pickling. It returns the pickled representation of an object as a byte object instead of writing it to the file. It is called to serialize an object hierarchy.


Syntax:


import pickle

pickle.dump(object, file, protocol)


where

the object is a python object to serialize

a file is a file object in which the serialized python object will be stored


protocol if not specified is 0. If specified as HIGHEST PROTOCOL or negative, then the highest protocol version available will be used. 



load()


The above function performs unpickling. It reads a pickled object from a binary file and returns it as an object. It is used to deserialize a data stream.


Syntax:


import pickle

pickle.load(file)



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