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Reading list

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
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 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

How to Use Python pyenv?

JanviKumari01
JanviKumari01 27 Jun, 2024
3 min read

Introduction

In the ever-evolving landscape of software program development, coping with multiple versions of Python can be a hard mission. Developers frequently discover themselves in conditions where exceptional initiatives require exclusive Python versions. This is where pyenv is available in handy. Pyenv is an easy yet effective tool that permits builders to without problems transfer among a couple of variations of  Python. This article will guide you via the system of putting in and the use of pyenv to manage Python versions on your machine.

Check this out: Python Tutorial | Learn Python For Data Science

How to Use Python pyenv?

Overview

  • Understand the need to use pyenv.
  • Learn to install pyenv on macOS, Linux, and Windows.
  • Learn how and when to use pyenv.

Table of Contents

Why Use pyenv?

The primary reason for the use of pyenv is to control multiple Python versions without interfering with the system Python. This is particularly useful for developers who work on various projects that require different Python environments. With pyenv, you can:

  1. Install Multiple Python Versions: Easily install and switch between multiple versions of Python.
  2. Isolate Project Environments: Create project-specific Python environments to avoid conflicts.
  3. Global and Local Python Versions: Set a global Python version and override it with a local version for specific projects.
  4. Simplify Development Workflow: Streamline your development process by managing dependencies and Python versions effortlessly.
how to manage multiple python versions

Installing pyenv

Before you can start using pyenv, you need to install it on your system. The installation process varies depending on your operating system.

Installation on macOS and Linux

Step 1: Install Prerequisites

First, you need to install the dependencies required by pyenv. On macOS, you can use Homebrew:

brew update
brew install pyenv

On Linux, you can use the package manager:

sudo apt update
sudo apt install -y make build-essential libssl-dev zlib1g-dev \
libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm \
libncurses5-dev libncursesw5-dev xz-utils tk-dev libffi-dev \
liblzma-dev python-openssl git

Step 2: Install pyenv

curl https://pyenv.run | bash

Step 3: Update Shell Configuration

Add the following lines to your shell configuration file (.bashrc, .zshrc, etc.):

export PATH="$HOME/.pyenv/bin:$PATH"
eval "$(pyenv init --path)"
eval "$(pyenv init -)"
eval "$(pyenv virtualenv-init -)"

Step 4: Restart Your Shell

Apply the changes by restarting your shell or running:

source ~/.bashrc

Installation on Windows

For Windows users, pyenv can be installed using the pyenv-win project. Follow these steps:

Step 1: Install Prerequisites

Ensure you have Git for Windows and a terminal like PowerShell or Git Bash.

 Step 2: Install pyenv-win

git clone https://github.com/pyenv-win/pyenv-win.git $HOME/.pyenv

Step 3: Update Environment Variables

Include the following in your environment variables (System Properties > Environment Variables):

Add $HOME\.pyenv\pyenv-win\bin to PATH.

Add $HOME\.pyenv\pyenv-win\shims to PATH.

Using pyenv

Once pyenv is installed, you can start managing Python versions.

Installing Python Versions

To install a specific Python version, use the following command:

pyenv install 3.8.10

Setting Global and Local Python Versions

Set a global Python version:

pyenv global 3.8.10

This version will be used by default. To set a local version for a specific project:

pyenv local 3.9.5

This creates a .python-version file in the project directory, specifying the Python version.

Managing Virtual Environments

pyenv integrates well with pyenv-virtualenv, allowing you to create virtual environments:

pyenv virtualenv 3.8.10 myenv

Activate the virtual environment:

pyenv activate myenv

Deactivate it when done:

pyenv deactivate

Conclusion

Pyenv is an invaluable device for developers who need to manage multiple Python variations and environments. By following the steps mentioned in this text, you could without problems install and use pyenv to streamline your development workflow, ensuring that every mission uses the precise Python model without conflicts. Embrace the power and control that pyenv gives, and enhance your Python skills.

If you wish to learn more about Python, here’s an introductory course from Analytics Vidhya: Introduction to Python for Data Science

Frequently Asked Questions

Q1. What is pyenv used for?

A. Pyenv is used to control more than one version of Python in your system. It lets you switch among specific Python versions for different projects without difficulty, making sure that each project has the right Python environment.

Q2. What is the difference between pyenv and pip?

A. pyenv is a tool for managing multiple Python versions. It allows you to install, switch, and manage different versions of Python on the same system. pip is a package manager for Python. It is used to install and manage Python packages (libraries and dependencies) within a specific Python environment.

Q3. How do I remove all versions of pyenv?

A. To remove all Python versions managed by pyenv, follow these steps:
1. Remove the pyenv installation directory:
rm -rf ~/.pyenv
2. Remove any references to pyenv in your shell configuration files (.bashrc, .zshrc, etc.).

BeginnerGuidepyenvpythonPython pyenvPython versions
JanviKumari01
JanviKumari01 27 Jun, 2024

Hi I am Janvi Kumari currently a Data Science Intern at Analytics Vidhya, passionate about leveraging data for insights and innovation. Curious, driven, and eager to learn. If you'd like to connect, feel free to reach out to me on LinkedIn

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