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R vs Python

(syntax ONLY, no winners here)

A repo comparing syntax in R and Python for various tasks. Not comprehensive, but a subset of lines to get one started

This is essentially a fork of a slide deck from Decision Stats

More geared for R users, trying out Python than otherwise. We use Rstudio and Rmarkdown to create the reference.

RStudio users, you may want to check out anaconda and Spyder

# Let us use conda to get all the packs we need
conda install pandas
## [1] "/Users/sahilseth/anaconda/bin:/Users/sahilseth/anaconda/bin:/usr/bin:/bin:/usr/sbin:/sbin:/usr/local/bin:/opt/X11/bin:/Library/TeX/texbin:/usr/texbin"
install.packages(c("e1071", "kknn", "randomForest", "rpart"))

# extra libs to compile this document
devtools::install_github("yihui/runr")

Resources:

Basic functions

Functions R Python
Downloading and installing a package install.packages('name') pip install name
Load a package library('name') import name as other_name
Checking working directory getwd() import os;os.getcwd()
Setting working directory setwd() os.chdir()
List files in a directory dir() os.listdir()
List all objects ls() globals()
Remove an object rm('name') del('object')

Data Frame

Creation

R

Creating a data frame df of dimension 6x4 (6 rows and 4 columns) containing random numbers

A <- matrix(runif(24,0,1), nrow=6, ncol=4)
df <- data.frame(A)
print(df)
##            X1          X2         X3        X4
## 1 0.008414911 0.311282917 0.61186960 0.8987637
## 2 0.021930034 0.072141218 0.96503840 0.9508710
## 3 0.227929049 0.840288003 0.61093433 0.3269284
## 4 0.162879566 0.325983315 0.82753045 0.4227151
## 5 0.593558020 0.009578978 0.84678802 0.2197988
## 6 0.219805626 0.054050339 0.04714518 0.1655515

Here,

  • runif function generates 24 random numbers between 0 to 1
  • matrix function creates a matrix from those random numbers, nrow and ncol sets the numbers of rows and columns to the matrix
  • data.frame converts the matrix to data frame | (Using pandas package*)

Python

import numpy as np
import pandas as pd
A=np.random.randn(6,4)
df=pd.DataFrame(A)
print(df)
##           0         1         2         3
## 0 -0.217405 -0.163276  0.936169 -0.089373
## 1  2.276137  0.891530  1.257429 -0.686684
## 2  0.295248 -0.528968  0.364880  0.274526
## 3  0.854174 -2.911316  0.768290  0.972371
## 4 -1.377254  2.524532 -0.718311  1.294197
## 5  0.252250 -0.408106 -0.598757  1.542085

Here,

  • np.random.randn generates a matrix of 6 rows and 4 columns; this function is a part of numpy library
  • pd.DataFrame converts the matrix in to a data frame

Inspecting and Viewing Data R/Python

Data.Frame Attributes

function R Python
number of rows rownames(df) df.index
number of coliumns colnames(df) df.index
first few rows head(df) df.head
last few rows tail(df) df.tail
get dimensions dim(df) df.shape
length of df length(df) df.len
same as number of columns

data.frame Summary

function R Python
quick summary including mean/std. dev etc summary(df) df.describe
setting row and column names rownames(df) = c("a", "b")
colnames(df) = c("x", "y")
df.index = ["a", "b"]
df.columns = ["x", "y"]

data.frame sorting data

function R Python
sorting the data df[order(df$x)] df.sort(['x'])

data.frame selection

function R Python
slicing a set of rows, from row number x to y df[x:y, ] df[x-1:y]
Python starts counting from 0
slicing a column names df[, "a"]
df$a
df["a"]
df.loc[:, ['a']]
slicing a column and rows df[x:y, x:y] df.iloc[x-1:y, a-1,b]
extract specific element df[x, y] df.iloc[x-1, y-1]

data.frame filtering/subsetting

function R Python
subset rows where x>5 subset(df, x>5) df[df.A> 5]

Math functions

function R Python
sum sum(x) math.fsum(x)
square root sqrt(x) math.sqrt(x)
standard deviation sd(x) numpy.std(x)
log log(x) math.log(x)
mean mean(x) numpy.mean(x)
median median(x) numpy.media(x)

Data Manipulation

function R Python
convert character to numeric as.numeric(x) for single values: int(x), long(x), float(x)
for list, vectors: map(int, x), map(long, x), map(float, x)
convert numeric to character as.character(x)
paste(x)
for single values: str(x)
for list, vectors: map(str, x)
check missing value is.na(x)
is.nan(x)
math.is.nan(x)
remove missing value na.omit(x) [x for x in list if str(x) != 'nan']
number of chars. in value char(x) len(x)

Date Manipulation

function R (lubridate) Python
Getting time and date Sys.time() d=datetime.date.time.now()
parsing date and time:
YYYY MM DD HH:MM:SS
lubridate::ymd_hms(Sys.time()) d.strftime("%Y %b %d %H:%M:%s")

Data Visualization

function R Python
Scatter Plot plot(variable1,variable2) import matplotlib
plt.scatter(variable1,variable2);plt.show()
Boxplot boxplot(Var) plt.boxplot(Var);plt.show()
Histogram hist(Var) plt.hist(Var) plt.show()
Pie Chart pie(Var) from pylab import *
pie(Var) show()

import matplotlib.pyplot as plt

Data Visualization: Bubble

Machine Learning

SVM on Iris Dataset

R

To know more about svm function in R visit: http://cran.r-project.org/web/packages/e1071/

library(e1071)
data(iris)
trainset = iris[1:149,]
testset = iris[150,]
svm.model = svm(Species~., data = trainset, cost = 100, gamma = 1)
svm.pred = predict(svm.model, testset)
svm.pred
##       150 
## virginica 
## Levels: setosa versicolor virginica

Python

To install sklearn library visit scikit-learn.org

To know more about sklearn svm visit sklearn.svm.SVC

from sklearn import svm
from sklearn import datasets

#Calling SVM
clf = svm.SVC()

iris = datasets.load_iris()

# Constructing training data X,
X, y = iris.data[:-1], iris.target[:-1]

# Fitting SVM
clf.fit(X, y)

# Testing the model on test data print
clf.predict(iris.data[-1])

# Output: Virginica Output: 2, corresponds to Virginica

Linear Regression

R

To know more about lm function in R visit: https://stat.ethz.ch/R-manual/R-devel/library/stats/html/lm.html

library(broom)

data(iris)

iris$y <- sapply(as.character(iris$Species), function(x){
  switch (x,
    setosa = 0,
    versicolor = 1,
    2
  )
})

train_set <- iris[1:149,]
test_set <- iris[150,]


fit <- lm(y ~ 0+Sepal.Length+ Sepal.Width +  Petal.Length+ Petal.Width , data=train_set)
tidy(fit)
##           term    estimate  std.error  statistic      p.value
## 1 Sepal.Length -0.07454598 0.04926761 -1.5130828 1.324352e-01
## 2  Sepal.Width -0.03465755 0.05695934 -0.6084611 5.438337e-01
## 3 Petal.Length  0.21590110 0.05664803  3.8112730 2.037526e-04
## 4  Petal.Width  0.60581643 0.09340629  6.4858203 1.301553e-09
coefficients(fit)
## Sepal.Length  Sepal.Width Petal.Length  Petal.Width 
##  -0.07454598  -0.03465755   0.21590110   0.60581643
predict.lm(fit, test_set)
##      150 
## 1.647771

Python

To know more about sklearn linear regression visit: http://scikit- learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html

from sklearn import linear_model
from sklearn import datasets
iris = datasets.load_iris()
regr = linear_model.LinearRegression()

X, y = iris.data[:-1], iris.target[:-1]

regr.fit(X, y)

print(regr.coef_)
print(regr.predict(iris.data[-1]))
## [-0.09726197 -0.05347337  0.21782359  0.61500051]
## [ 1.65708429]

Random forest

R

To know more about randomForest package in R visit: http://cran.r-project.org/web/packages/randomForest/

library(randomForest)
## randomForest 4.6-12
## Type rfNews() to see new features/changes/bug fixes.
iris.rf <- randomForest(y ~ .,  data=train_set,ntree=100,importance=TRUE, proximity=TRUE)
## Warning in randomForest.default(m, y, ...): The response has five or fewer
## unique values. Are you sure you want to do regression?
print(iris.rf)
## 
## Call:
##  randomForest(formula = y ~ ., data = train_set, ntree = 100,      importance = TRUE, proximity = TRUE) 
##                Type of random forest: regression
##                      Number of trees: 100
## No. of variables tried at each split: 1
## 
##           Mean of squared residuals: 0.01151123
##                     % Var explained: 98.27
predict(iris.rf, test_set, predict.all=TRUE)
## $aggregate
##      150 
## 1.963167 
## 
## $individual
##     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## 150    2    2    2    2    2    2    2    2    2     2     2     2     2
##     [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24]
## 150     2   1.8     2     2     2     2     2     2     2     2     2
##     [,25] [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35]
## 150     2     2     2     2     2     2     2     2     2     2     2
##     [,36] [,37] [,38] [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46]
## 150     2     2     2     2     2   1.8     2     2     2     2     2
##     [,47] [,48] [,49] [,50] [,51] [,52] [,53] [,54] [,55] [,56] [,57]
## 150     2     2     2     2     2     2     1     2     2     2     2
##     [,58] [,59] [,60] [,61] [,62] [,63] [,64] [,65] [,66] [,67] [,68]
## 150     2     2     2     2     2     2     2     2     2     2     2
##     [,69] [,70] [,71] [,72] [,73] [,74] [,75] [,76] [,77] [,78] [,79]
## 150     2     2   1.8     2     2     2     2     2     2     2     2
##     [,80] [,81] [,82] [,83] [,84] [,85] [,86] [,87] [,88] [,89]    [,90]
## 150     2     2     2     2     2     1     2     2     2     2 1.666667
##     [,91] [,92] [,93] [,94] [,95] [,96] [,97] [,98] [,99] [,100]
## 150     2     2     2     2  1.25     2     2     2     2      2

Python

To know more about sklearn random forest visit: http://scikit- learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html

from sklearn import ensemble
from sklearn import datasets

clf = ensemble.RandomForestClassifier(n_estimators=100, max_depth=10)
iris = datasets.load_iris()
X, y = iris.data[:-1], iris.target[:-1]

clf.fit(X, y)
print(clf.predict(iris.data[-1]))

# Output: 1.845 Output: 2
## [2]

Decision Tree

R

To know more about rpart package in R visit: http://cran.r-project.org/web/packages/rpart/

library(rpart)
data(iris)

sub = c(1:149)

fit = rpart(Species ~., data = iris, subset = sub)

pred = predict(fit, iris[sub, ], type = "class")

Python

To know more about sklearn desicion tree visit : http://scikit- learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html

from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier

clf = DecisionTreeClassifier(random_state=0)
iris = load_iris()

X, y = iris.data[:-1], iris.target[:-1]

clf.fit(X, y)

print(clf.predict(iris.data[-1]))

#Output: Virginica Output: 2, corresponds to virginica
## [2]

Gaussian Naive Bayes

R

To know more about e1071 package in R visit: http://cran.r-project.org/web/packages/e1071/

library(e1071)
data(iris)
trainset = iris[1:149,]
testset = iris[150,]
classifier = naiveBayes(trainset[,1:4], trainset[, 5])

predict(classifier, testset[,5])
## [1] setosa
## Levels: setosa versicolor virginica

Python

To know more about sklearn Naive Bayes visit : http://scikit- learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html

from sklearn.datasets import load_iris
from sklearn.naive_bayes import GaussianNB

clf = GaussianNB()
iris = load_iris()
X, y = iris.data[:-1], iris.target[:-1]
clf.fit(X, y)
print(clf.predict(iris.data[-1]))
#Output: Virginica Output: 2, corresponds to virginica
## [2]

K Nearest Neighbours

R

To know more about kknn package in R visit:

library(kknn)
data(iris)

trainset <- iris[1:149,]
testset = iris[150,] 

iris.kknn = kknn(Species~.,  trainset,testset, distance = 1, kernel = "triangular")

summary(iris.kknn)
## 
## Call:
## kknn(formula = Species ~ ., train = trainset, test = testset,     distance = 1, kernel = "triangular")
## 
## Response: "nominal"
##         fit prob.setosa prob.versicolor prob.virginica
## 1 virginica           0        0.232759       0.767241
fit <- fitted(iris.kknn)
fit
## [1] virginica
## Levels: setosa versicolor virginica

Python

To know more about sklearn k nearest neighbors visit: scikitlearn.org

from sklearn.datasets import load_iris
from sklearn.neighbors import KNeighborsClassifier

knn = KNeighborsClassifier()

iris = load_iris()

X, y = iris.data[:-1], iris.target[:-1]

knn.fit(X,y)

print(knn.predict(iris.data[-1]))

# Output: Virginica Output: 2, corresponds to virginica

playing with class/objects

writing functions

debugging

creating packages

Getting help on function

#py$stop()

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A repo comparing syntax in R and Python for various tasks. Not comprehensive, but a subset of lines to get one started

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