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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Using AWS S3 with Python boto3

Ansaba
Ansaba Last Updated : 29 Dec, 2022
5 min read

This article was published as a part of the Data Science Blogathon.

AWS S3
Source: https://python.plainenglish.io/rename-and-move-s3-object-using-python-boto3-80bd07075e47

Introduction

AWS S3 is one of the object storage services offered by Amazon Web Services or AWS. It allows users to store and retrieve files quickly and securely from anywhere. Users can combine S3 with other services to build numerous scalable applications. Boto is the Python SDK (software development kit) or hand-coded Python library for AWS. Its different versions include Boto2, Boto3, and Botocore. The latest version of the SDK is Boto3 which is the ground-up rewrite of Boto. Through the boto3 python library, users can connect to Amazon services, including S3, and use the resources from within AWS. It helps developers to create, configure, and manage AWS services, making it easy to integrate with Python applications, libraries, or scripts. This article covers how boto3 works and how it helps interact with S3 operations such as creating, listing, and deleting buckets and objects.

What is boto3

Boto3 is a Python SDK or library that can manage and access various services of AWS, such as Amazon S3, EC2, Dynamo DB, SQS, Cloudwatch, etc., through python scripts. Boto3 has a data-driven approach for generating classes at runtime from JSON description files shared between SDKs. Because Boto 3 is generated from these shared JSON files, users get fast updates to the latest services and a consistent API across services. It provides object-oriented and easy-to-use API as well as low-level direct service access.

Key Features of boto3

  • It is built on top of botocore- a Python library used to send API requests to AWS and receive responses from the service.
  • Supports Python 2.7+ and 3.4+ natively.
  • Boto3 provides sessions and per-session credentials & configuration, along with essential components like authentication, parameter, and response handling.
  • Has a consistent and Up-to-date Interface

 

Working with AWS S3 and Boto3

AWS S3

Source: https://dashbird.io/blog/boto3-aws-python/

 

Using the Boto3 library or SDK with Amazon S3 allows users to create, delete, and update S3 Buckets, Objects, S3 Bucket Policies, etc., from Python programs or scripts in a faster way. Boto3 has two abstractions, namely client and resource. Users can choose client abstraction if they want to work with single S3 files or resource abstraction if they want to work with multiple S3 buckets. Clients provide a low-level interface to the AWS services, whereas resources are higher-level abstraction than clients.

Installation of boto3 and Building AWS S3 Client

Installing boto3 to your application:
On the Terminal, use the code
pip list
The above code will list the installed packages. If Boto3 is not installed, install it by the following code.
pip install boto3
Build an S3 client to access the service methods:
Create an S3 client that helps access the objects stored in the S3 environment and set credentials, including aws_access_key_id and aws_secret_access_key. It is essential to have credentials such as Access Key and Secret Key to access the S3 bucket and to run the following code.
# Import the necessary packages
import boto3
# Now, build a client
S3 = boto3.client(
    's3',
    aws_access_key_id = 'enter your_aws_access_key_id ',
    aws_secret_access_key = ' enter your_aws_secret_access_key ',
    region_name = ' enter your_aws_region_name '
)

 

AWS S3 Operations With boto3

 

AWS S3

Creating buckets:

To create an S3 bucket, use the create_bucket() method with the Bucket and ACL parameters. ACL represents Access Control List which manages access to S3 buckets and objects. It is important to note that Bucket names should be unique throughout the whole AWS platform.
my_bucket = "enter your s3 bucket name that has to be created"
bucket = s3.create_bucket(
ACL='private',
Bucket= my_bucket
)
Listing buckets:
To list all the available buckets, use the list_buckets() method.
bucket_response = s3.list_buckets()
# Output the bucket names
print('Existing buckets are:')
for bucket in bucket_response ['Buckets']:
    print(f'  {bucket["Name"]}')
Deleting Buckets:
A bucket in S3 can be deleted using the delete_bucket() method. The bucket must be empty, meaning it does not contain any objects to perform the deletion.
my_bucket = "enter your s3 bucket name that has to be deleted"
response = s3.delete_bucket(Bucket= my_bucket)
print("Bucket has been deleted successfully !!!")
Listing the files from a bucket:
Files or objects from an S3 bucket can be listed using the list_objects method or the list_objects_v2 method.
my_bucket = "enter your s3 bucket name from which objects or files has to be listed out"
response = s3.list_objects(Bucket= my_bucket,
                           MaxKeys=10, 
                           Preffix="only_files_starting_with_this_string")
MaxKeys argument represents the maximum number of objects to be listed. The prefix argument lists Objects whose keys (names) only start with a specific prefix.
Another way to list objects:
    s3 = boto3.client("s3")
    my_bucket = " enter your s3 bucket name from which objects or files has to be listed out "
    response = s3.list_objects_v2(Bucket=my_bucket)
    files = response.get("Contents")
    for file in files:
        print(f"file_name: {file['Key']}, size: {file['Size']}")
Uploading files:
To upload a file to an s3 bucket, use the method upload_file () having the following parameters:
  • File: it defines the path of the file to be uploaded
  • Key: it represents the unique identifier for an object within a bucket
  • Bucket: bucket name to which file has to be uploaded
my_bucket = "enter your bucket name to which files has to be uploaded"
file_name = "enter your file path name to be uploaded"
key_name = "enter unique identifier"
s3.upload_file(Filename= file_name, Bucket= my_bucket, Key= key_name)
Downloading files:
To download a file or object locally from a bucket, use the download_file() method with Key, Bucket, and Filename parameters.
my_bucket = "enter your s3 bucket name from which object or files has to be downloaded"
file_name = "enter file to be downloaded"
key_name = "enter unique identifier"
s3.download_file(Filename= file_name, Bucket= my_bucket, Key= key_name)
Deleting files:
To delete a file or object from a bucket, use the delete_object() method with Key and Bucket parameters.
my_bucket = "enter your s3 bucket name from which objects or files has to be deleted"
key_name = "enter unique identifier"
s3.delete_object(Bucket= my_bucket, Key= key_name)
Get the object’s metadata:
To get the file or object’s details, such as last modification time, storage class, content length, size in bytes, etc., use the head_object() method with Key and Bucket parameters.
my_bucket = "enter your s3 bucket name from which objects or file's metadata has to be obtained"
key_name = "enter unique identifier"
response = s3.head_object(Bucket= my_bucket, Key= key_name)

Conclusion

AWS S3 is one of the most reliable, flexible, and durable object storage systems that allows users to store and retrieve data. AWS defines boto3 as a Python library or SDK (Software Development Kit) to create, manage and configure AWS services, including S3. The boto3 operates AWS services in a programmatic way from your applications and services.
Key Takeaways:
  • AWS S3 is one object storage service that helps store and retrieve files quickly.
  • Boto3 is a Python SDK or library that can manage Amazon S3, EC2, Dynamo DB, SQS, Cloudwatch, etc.
  • Boto3 clients provide a low-level interface to the AWS services, whereas resources are a higher-level abstraction than clients.
  • Using the Boto3 library with Amazon S3 allows users to create, list, delete, and update S3 Buckets, Objects, S3 Bucket Policies, etc., from Python programs or scripts in a faster way.

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Ansaba
Ansaba

I'm a cloud enthusiast and have done graduation in computer science. My skills include C, C++, Php, Python, Cloud services(AWS, GCP), shell scripting, MySQL, etc. I have hands-on experience and have done several projects and certifications in areas of Python, Php, MySQL, Cloud (GCP, AWS), and C++

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