lifelong learner
See Datacamp article.
Six different uses of underscore(_)
https://github.com/pyenv-win/pyenv-win
commands List all available pyenv commands local Set or show the local application-specific Python version global Set or show the global Python version shell Set or show the shell-specific Python version install Install 1 or more versions of Python uninstall Uninstall 1 or more versions of Python update Update the cached version DB rehash Rehash pyenv shims (run this after switching Python versions) vname Show the current Python version version Show the current Python version and its origin version-name Show the current Python version versions List all Python versions available to pyenv exec Runs an executable by first preparing PATH so that the selected Python which Display the full path to an executable whence List all Python versions that contain the given executable
https://towardsdatascience.com/managing-virtual-environment-with-pyenv-ae6f3fb835f8
This content of this book is extremely useful resource for learning and understand statistical concepts and techniques. It is great to see how Python and R codes are implemented for each concept, but only the snippet of codes are provided on many examples in the book. Fortunately, the publisher provides the whole codes by chapter. The widely used Python packages (pandas, numpy, scipy, statsmodels, sklearn, matplotlib, seaborn, and more) and R libraries can be easily located in each chapter and index.
R libraries used:
library(boot) #Bootstrap Functions
library(ca) #Simple, Multiple and Joint Correspondence Analysis
library(cluster) #”Finding Groups in Data”: Cluster Analysis
library(corrplot) #Visualization of a Correlation Matrix
library(dplyr) #A Grammar of Data Manipulation
library(ellipse) #Functions for Drawing Ellipses and Ellipse-Like Confidence Regions
library(FNN) #Fast Nearest Neighbor Search Algorithms and Applications
library(ggplot2) #Create Elegant Data Visualisations Using the Grammar of Graphics
library(gmodels) #Various R Programming Tools for Model Fitting
library(klaR) #Classification and Visualization
library(lmPerm) #Permutation Tests for Linear Models
library(lubridate) #Make Dealing with Dates a Little Easier
library(MASS) #Support Functions and Datasets for Venables and Ripley’s MASS
library(matrixStats) #Functions that Apply to Rows and Columns of Matrices (and to Vectors)
library(mclust) #Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation
library(mgcv) #Mixed GAM Computation Vehicle with Automatic Smoothness Estimation
library(pwr) #Basic Functions for Power Analysis
library(randomForest) #Breiman and Cutler’s Random Forests for Classification and Regression
library(rpart) #Recursive Partitioning and Regression Trees
library(tidyr) #Tidy Messy Data
library(vioplot) #Violin Plot
library(xgboost) #Extreme Gradient Boosting
This book is published in 2017. The fundamentals of Python language is covered in this book. O’Reilly offers companion videos.
This book is a little outdated, but great for the beginners for grasping fundamentals. The companion video uses a text editor and Python on command line environment. It is great for learning how to use command-line arguments, but not much useful anymore since Jupyter notebook became dominant in the market.
