R, Python 분석과 프로그래밍의 친구 (by R Friend)

In [1]: import numpy as np

   ...: x = np.arange(12).reshape(3, 4)

   ...: x

   ...:

Out[1]:

array([[ 0, 1, 2, 3],

        [ 4, 5, 6, 7],

        [ 8, 9, 10, 11]])

(1) np.ravel(x, order='C') : C와 같은 순서로 인덱싱하여 평평하게 배열 (디폴트)

In [2]: np.ravel(x, order='C') # by default

   ...:

Out[2]: array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])

(2) np.ravel(x, order='F') : Fortran과 같은 순서로 인덱싱하여 평평하게 배열

In [3]: np.ravel(x, order='F')

   ...:

Out[3]: array([ 0, 4, 8, 1, 5, 9, 2, 6, 10, 3, 7, 11])

 (3) np.ravel(x, order='K') : 메모리에서 발생하는 순서대로 인덱싱하여 평평하게 배열

In [4]: np.ravel(x, order='K')

   ...:

Out[4]: array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])

(4) np.raver(y, order='C') : 3차원 배열의 평평하게 펴기

In [5]: y = np.arange(12).reshape(2, 3, 2)

   ...: y

Out[5]:

array([[[ 0, 1],

         [ 2, 3],

         [ 4, 5]],

        [[ 6, 7],

         [ 8, 9],

         [10, 11]]])

In [6]: np.ravel(y, order='C')

   ...:

Out[6]: array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])

 (5) np.ravel(z, order='K') : 축이 바뀐 3차원 배열을 평평하게 펴기

In [7]: z = np.arange(12).reshape(2, 3, 2).swapaxes(1, 2)

In [8]: z

Out[8]:

array([[[ 0, 2, 4],

         [ 1, 3, 5]],

        [[ 6, 8, 10],

         [ 7, 9, 11]]])

In [9]: np.ravel(z, order='K')

   ...:

Out[9]: array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])

  (1) if, elif, else 조건문을 사용한 FizzBuzz 코딩

# (1) if, elif, and else

for num in range(1, 31):

    if num % 3 == 0 and num % 5 == 0:

        print('FizzBuzz')

    elif num % 3 == 0:

        print('Fizz')

    elif num % 5 == 0:

        print('Buzz')

    else:

        print(num)

  (2) if, elif, else 조건문과 사용자 정의 함수(UDF)을 사용한 FizzBuzz 코딩

# (2) User Defined Function

def fizzbuzz(num):

    if num % 3 == 0 and num % 5 == 0:

        return 'FizzBuzz'

    elif num % 3 == 0:

        return 'Fizz'

    elif num % 5 == 0:

        return 'Buzz'

    else:

        return str(num)

print("\n".join(fizzbuzz(num) for num in range(1, 31)))

  (3) 문자열 합치기(Concatenating Strings)을 이용한 FizzBuzz 코딩

# (3) Concatenating Strings

for num in range(1, 31):

    string = ""

    if num % 3 == 0:

        string = string + "Fizz"

    if num % 5 == 0:

        string = string + "Buzz"

    if num % 5 != 0 and num % 3 != 0:

        string = string + str(num)

    print(string)

> rm(list=ls()) # clean all

# Sample vector

> set.seed(123) # for reproducibility

> vec_raw <- sample(c(0, 1), size=30, replace=TRUE, prob=(c(0.8, 0.2)))

> vec_raw

 [1] 0 0 0 1 1 0 0 1 0 0 1 0 0 0 0 1 0 0 0 1 1 0 0 1 0 0 0 0 0 0

> vec <- vec_raw # copy for manipulation

##---------------------------------------------

# Vector Bin Split by successive '0's criteria

##---------------------------------------------

# Setting

vec_tmp <- c() # null vector

bin_range <- 3 # successive '0's criterion

vec_idx <- 1

vec_num <- 1

# Zero_Bin_Splitter

while (length(vec) > 0){

  if (sum(vec[1:bin_range], na.rm=T) != 0){

    vec_tmp[vec_idx] <- vec[1]

    vec <- vec[-1]

    vec_idx <- vec_idx + 1

  } else if (is.null(vec_tmp)){

    assign(paste0("vec_split_", vec_num), vec[1:bin_range])

    vec <- vec[-(1:bin_range)]

    vec_idx <- 1 # initialization

    vec_num <- vec_num + 1

  } else {

    assign(paste0("vec_split_", vec_num), vec_tmp)

    vec_tmp <- c() # initialization

    vec_num <- vec_num + 1

    assign(paste0("vec_split_", vec_num), vec[1:bin_range])

    vec <- vec[-(1:bin_range)]

    vec_idx <- 1 # initialization

    vec_num <- vec_num + 1

  }

}

# delete temp vector and arguments

rm(vec, vec_tmp, bin_range, vec_idx, vec_num)

# by June Young Lee

library(data.table)

# 0이 세번 연속으로 들어 있는 아무 벡터 생성

vec <- c(0,0,0,3,10,2,3,0,4,0,0,0,1,2,50,4,0,0,32,1,0,0,0,1,1)

# data.table함수인 rleid이용하여 연속인 행들을 구분하는 idx1 컬럼 생성 

dt <- data.table(v1=vec, idx1=rleid(vec))

# idx1 기준으로 갯수를 세어 N 컬럼 생성 

dt[,N:=.N, by=idx1]

# 0이 세번 연속으로 나온 그룹(=N컬럼변수가 3인)을 idx2로 체크하기 

dt[v1==0&N==3,idx2:=1L]

# split 하기 위한 기준 벡터를 마찬가지로 rleid함수를 이용하여 idx3로 생성

dt[,idx3:=rleid(idx2)]

# data.table의 split 함수 적용하고, lapply 적용하여 v1 칼럼만 추출 

res <- lapply(split(dt, by="idx3"), function(x) x[,v1])

res

# $`1`

# [1] 0 0 0

# 

# $`2`

# [1] 3 10 2 3 0 4

# 

# $`3`

# [1] 0 0 0

# 

# $`4`

# [1] 1 2 50 4 0 0 32 1

# 

# $`5`

# [1] 0 0 0

# 

# $`6`

# [1] 1 1

# 0이 3번 연속으로 나온 놈들을 남겨두도록 작성한 이유는, 

# 위치확인 & 확인용입니다. 

# 필요없으면, 아래 정도의 코드를 추가하면 되겠네요.

res2 <- res[!unlist(lapply(res, function(x) length(x)==3&sum(x)==0))]

res2

# $`2`

# [1] 3 10 2 3 0 4

# 

# $`4`

# [1] 1 2 50 4 0 0 32 1

# 

# $`6`

# [1] 1 1

In [1]: import numpy as np

In [2]: x = np.arange(12).reshape(3, 4)

In [3]: x.shape

Out[3]: (3, 4)

In [4]: x

Out[4]:

array([[ 0, 1, 2, 3],

        [ 4, 5, 6, 7],

        [ 8, 9, 10, 11]])

  (1) reshape(-1, 정수) 의 행(row) 위치에 '-1'이 들어있을 경우

x.reshape(-1, 1)

=> shape(12,1)

x.reshape(-1, 2) 

=> shape(6, 2)

 x.reshape(-1, 3)

=> shape(4, 3)

x.reshape(-1, 4) 

=> shape(3, 4)

In [5]: x.reshape(-1, 1)

Out[5]:

array([[ 0],

        [ 1],

        [ 2],

        [ 3],

        [ 4],

        [ 5],

        [ 6],

        [ 7],

        [ 8],

        [ 9],

        [10],

        [11]])

 In [6]: x.reshape(-1, 2)

Out[6]:

array([[ 0, 1],

        [ 2, 3],

        [ 4, 5],

        [ 6, 7],

        [ 8, 9],

        [10, 11]])

 In [7]: x.reshape(-1, 3)

Out[7]:

array([[ 0, 1, 2],

        [ 3, 4, 5],

        [ 6, 7, 8],

        [ 9, 10, 11]])

 In [8]: x.reshape(-1, 4)

Out[8]:

array([[ 0, 1, 2, 3],

        [ 4, 5, 6, 7],

        [ 8, 9, 10, 11]])

  (2) reshape(정수, -1) 의 열(column) 위치에 '-1'이 들어있을 경우

x.reshape(1, -1)

  => shape(1, 12)

In [9]: x.reshape(1, -1)

Out[9]: array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]])

 x.reshape(2, -1)

  => shape(2, 6)

In [10]: x.reshape(2, -1)

Out[10]:

array([[ 0, 1, 2, 3, 4, 5],

        [ 6, 7, 8, 9, 10, 11]])

 x.reshape(3, -1)

  => shape(3, 4)

In [11]: x.reshape(3, -1)

Out[11]:

array([[ 0, 1, 2, 3],

        [ 4, 5, 6, 7],

        [ 8, 9, 10, 11]])

 x.reshape(4, -1)

  => shape(4, 3)

In [12]: x.reshape(4, -1)

Out[12]:

array([[ 0, 1, 2],

        [ 3, 4, 5],

        [ 6, 7, 8],

        [ 9, 10, 11]])

  (3) reshape(-1) 인 경우

x.reshape(-1)

In [13]: x.reshape(-1)

Out[13]: array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])

 x.reshape(1, -1)

x.reshape(1, -1)

Out[14]: array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11]])

  (4) ValueError: cannot reshape array of size 12 into shape (5)

In [15]: x.reshape(-1, 5)

Traceback (most recent call last):

File "<ipython-input-15-3341ca33497d>", line 1, in <module>

x.reshape(-1, 5)

ValueError: cannot reshape array of size 12 into shape (5)

In [16]: x.reshape(7, -1)

Traceback (most recent call last):

File "<ipython-input-16-c8e97ae7c9bc>", line 1, in <module>

x.reshape(7, -1)

ValueError: cannot reshape array of size 12 into shape (7,newaxis)

  (5) ValueError: can only specify one unknown dimension

In [17]: x.reshape(-1, -1)

Traceback (most recent call last):

File "<ipython-input-17-8142d87a8f95>", line 1, in <module>

x.reshape(-1, -1)

ValueError: can only specify one unknown dimension

# KRUG's plot quiz, as of 18th NOV. 2017

library(ggplot2)

#install.packages("dplyr")

library(dplyr)

str(mtcars)

# making 'model' variable

mtcars$model <- rownames(mtcars)

# ordering by cyl, hp

mtcars_ord <- arrange(mtcars[,c('model', 'cyl', 'hp')], cyl, desc(hp))

# marking an inflection point within cluster : threshold >= 40

mtcars_cyl <- c(4, 6, 8)

mtcars_ord_2 <- data.frame()

for (i in 1:length(mtcars_cyl)) {

  mtcars_tmp <- subset(mtcars_ord, subset = (cyl == mtcars_cyl[i]))

  for (j in 1:nrow(mtcars_tmp)) {

    if (j != nrow(mtcars_tmp) & mtcars_tmp$hp[j] - mtcars_tmp$hp[j+1] >= 40) {

      mtcars_tmp$hp_outlier[j] = '1_outlier'

    } else {

      mtcars_tmp$hp_outlier[j] = '2_normal'

    }

  }

  mtcars_ord_2 <- rbind(mtcars_ord_2, mtcars_tmp)

  rm(mtcars_tmp)

}

# converting cyl variable type from numeric to factor

mtcars_ord_2$cyl_cd <- paste0("cyl :", mtcars_ord_2$cyl)

model_order <- mtcars_ord_2$model[order(mtcars_ord_2$cyl_cd, 

                                        mtcars_ord_2$hp, 

                                        decreasing = FALSE)]

mtcars_ord_2$model <- factor(mtcars_ord_2$model, levels = model_order)

# drawing cleveland dot plot

ggplot(mtcars_ord_2, aes(x = hp, y = model)) +

  geom_point(size = 2, aes(colour = hp_outlier)) +

  scale_colour_manual(values = c("red", "black")) + 

  theme_bw() +

  facet_grid(. ~ cyl_cd, scales = "free_y", space = "free_y") +

  xlim(0, max(mtcars_ord_2$hp)) +

  geom_hline(yintercept = nrow(mtcars_ord_2[mtcars_ord_2$cyl == 4,]) + 0.5, 

             colour = "black", linetype = "dashed", size = 0.5) +

  geom_hline(yintercept = nrow(mtcars_ord_2) - 

               nrow(mtcars_ord_2[mtcars_ord_2$cyl == 8,]) + 0.5, 

             colour = "black", linetype = "dashed", size = 0.5) +

  theme(legend.position = 'none')

library(data.table)
library(dplyr)
library(ggplot2)

mtcars %>%
mutate(car.name=rownames(.)) %>%
arrange(cyl, hp) %>%
mutate(order.key=1:n()) -> data

data %>%
ggplot(aes(x=hp, y=reorder(car.name, order.key))) +
geom_point(
colour=case_when(
data$car.name %in% c('Ferrari Dino','Maserati Bora') ~ 'red', 
TRUE ~ 'black')) +
geom_hline(yintercept = 11.5, linetype='dashed') +
geom_hline(yintercept = 18.5, linetype='dashed') +
facet_wrap(~ cyl, labeller = label_both) +
scale_x_continuous(limits=c(0,max(data$hp))) +
theme_bw() +
theme(axis.title.y=element_blank())

 (1) while: 조건이 참인 동안 반복 (loop while the condition is True)

while condition:

    conditional code block 

while 반복문

실행 결과 (result)

count = 0

while count < 5:

    print('count : ', count)

    count = count + 1

print('The end')

count : 0

count : 1

count : 2

count : 3

count : 4

The end

while 반복문 

실행 결과 (result) 

count = 0

while count < 5:

    print(count, ' is less than 5')

    count = count + 1

else:

    print(count, ' is not less 5 so stop here') 

0  is less than 5

1  is less than 5

2  is less than 5

3  is less than 5

4  is less than 5

5  is not less 5 so stop here

while 반복문 (infinite loop)

실행 결과 (result) 

 # Infinite loop

while(True):

    print('Infinite loop goes on and on ...')

Infinite loop goes on and on ...

Infinite loop goes on and on ...

Infinite loop goes on and on ...

File "zmq/backend/cython/checkrc.pxd", line 12, in zmq.backend.cython.checkrc._check_rc (zmq\backend\cython\socket.c:9621)

KeyboardInterrupt

 while 반복문 (with if, elif, else & break)

실행 결과 (result) 

while True:

    print('continue or stop? [continue/stop]:')

    answer = input()

    if answer == 'continue':

        print('>> continue again!')

    elif answer == 'stop':

        print('>> stop here!')

        break

    else:

        print('>> wrong answer!')

continue or stop? [continue/stop]:

continue

>> continue again!

continue or stop? [continue/stop]:

repeate

>> wrong answer!

continue or stop? [continue/stop]:

continue

>> continue again!

continue or stop? [continue/stop]:

stop

>> stop here!

 (2) for:  순서열의 처음부터 끝까지 반복 (loop until the end of the sequence)

for iterating_var in sequence:

    code block 

 for i in range(start, end, interval)

실행 결과 (result) 

for i in range(0, 5, 1): # (start, end, interval)

    print(i)

0

1

2

3

4

for i in range(3, 10, 2):

    print(i)

3

5

7

9

for i in range(0, 5): # interval default : 1

    print(i)

0

1

2

3

4

for i in range(5): # start default : 0

    print(i)

0

1

2

3

4

for i in range(5, 5):

    print(i)

(no result)

# TypeError

for i in range(4.9, 5):

    print(i)

Traceback (most recent call last):

  File "<ipython-input-11-bb2a792a3440>", line 1, in <module>

    for i in range(4.9, 5):

TypeError: 'float' object cannot be interpreted as an integer

for i in range() 복수 반복문

실행 결과 (result) 

for i in range(1, 6):

    for j in range(i+1, 6):

        print(i, '+', j, '=', i+j)

1 + 2 = 3

1 + 3 = 4

1 + 4 = 5

1 + 5 = 6

2 + 3 = 5

2 + 4 = 6

2 + 5 = 7

3 + 4 = 7

3 + 5 = 8

4 + 5 = 9

for i in tuple

실행 결과 (result) 

for i in ('Python', 'is', 'easy'):

    print(i)

print('Really?')

Python

is

easy

Really?

for loop with range() & tuple index

실행 결과 (result) 

# iterating by sequence index

mytuple = ('Python', 'is', 'easy')

for index in range(len(mytuple)):

    print(mytuple[index])

print('Really?')

Python

is

easy

Really? 

for i in list

실행 결과 (result) 

for mylist in ['Python', 'is', 'easy']:

    print(mylist)

print('Are you sure?') 

Python

is

easy

Are you sure? 

for i in string

실행 결과 (result) 

for mystr in ('Python is easy'):

    print(mystr)

print('--The end of string character--')

P

y

t

h

o

n

i

s

e

a

s

y

--The end of string character--

for i in dictionary

실행 결과 (result) 

mydic = {'cust_01': 'kim', 

            'cust_02': 'lee', 

            'cust_03': 'choi'}

for key, val in mydic.items(): # tuple unpacking

    print("ID is '{0}' and NAME is '{1}'".format(key, val))

ID is 'cust_01' and NAME is 'kim'

ID is 'cust_02' and NAME is 'lee'

ID is 'cust_03' and NAME is 'choi'

3. continue: 조건이 참이면 반복문 계속 (continue loop if the condition is True)

     break: 조건이 참이면 반복문 중단 (stop loop if the condition is True)

for 반복문 with continue 제어문

실행 결과 (result) 

# print only odd numbers b/w 1 ~ 5

for i in range(1, 6):

    if i % 2 == 0:

        continue

    print(i)

1

3

5

for 반복문 with continue 제어문

실행 결과 (result) 

# if i+j == 5 then print i, j combination

for i in range(1, 6):

    for j in range(i+1, 6):

        if i+j != 5:

            continue        

        print(i, '+', j, '=', i+j)

1 + 4 = 5

2 + 3 = 5

for 반복문 with break 제어문

실행 결과 (result) 

for i in range(1, 6):

    print(i)

    if i == 4:

        break

1

2

3

4

for 반복문 with break, if, else

실행 결과 (result) 

for i in range(3, 21):

    for j in range(2, i):

        if i % j == 0:

            k = i / j

            print("%d is %d * %d" %(i, j, k))

            break

        else:

            if j != i - 1:

                continue

            else:

                print(i, "is prime number.")

                break

print("The End")

3 is prime number.

4 is 2 * 2

5 is prime number.

6 is 2 * 3

7 is prime number.

8 is 2 * 4

9 is 3 * 3

10 is 2 * 5

11 is prime number.

12 is 2 * 6

13 is prime number.

14 is 2 * 7

15 is 3 * 5

16 is 2 * 8

17 is prime number.

18 is 2 * 9

19 is prime number.

20 is 2 * 10

The End

nested while loops with break, if

실행 결과 (result) 

i = 3

while(i < 21):

    j = 2

    while(j <= (i/j)):

        if not(i%j): 

            print('%d is %d * %d' %(i, j, i/j))

            break

        j = j + 1

    if (j > i/j):

        print(i, " is prime")

    i = i + 1

print('The end')

# True or False?

# not(4%2) # True

# not(3%2) # False

3  is prime

4 is 2 * 2

5  is prime

6 is 2 * 3

7  is prime

8 is 2 * 4

9 is 3 * 3

10 is 2 * 5

11  is prime

12 is 2 * 6

13  is prime

14 is 2 * 7

15 is 3 * 5

16 is 2 * 8

17  is prime

18 is 2 * 9

19  is prime

20 is 2 * 10

The end

구분

제품명/ 스펙 

 가격 (단위: 원)

 가격 합계

  데스크탑

  본체

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(배송비 3,000 별도) 

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 RAM 16GB 추가 (1개)

138,000 

67,900 

23,000 

149,900 

22,900 

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 IPTIME A2000UA-4dbi USB무선랜카드 

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  31,300 

(배송비 2,500 별도) 

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264,000 (배송 무료) 

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 키보드

125,800 (배송 무료) 

 (1) 문자열을 특정 길이로 만들고, 빈 자리수만큼 '0'을 채우기 : sprintf("%05d", var)

> # making a sample DataFrame

> df <- data.frame(var1 = c(1, 11, 111, 1111))

> df

  var1

1    1

2   11

3  111

4 1111

> #-------------------------

> # (1) sprintf(%03d, var) : Format number as fixed width, with leading zeros

> df <- transform(df, 

+                 var1_01d = sprintf("%01d", var1), 

+                 var1_02d = sprintf("%02d", var1), 

+                 var1_03d = sprintf("%03d", var1), 

+                 var1_04d = sprintf("%04d", var1), 

+                 var1_05d = sprintf("%05d", var1))

> 

> df

  var1 var1_01d var1_02d var1_03d var1_04d var1_05d

1    1        1       01      001     0001    00001

2   11       11       11      011     0011    00011

3  111      111      111      111     0111    00111

4 1111     1111     1111     1111     1111    01111

> View(df)

> sapply(df, class)

     var1  var1_01d  var1_02d  var1_03d  var1_04d  var1_05d

"numeric"  "factor"  "factor"  "factor"  "factor"  "factor"

 (2) 소수점 숫자(numeric)의 자리수를 지정해주기 : sprintf(".5f", var)

> #-------------------------

> # (3) sprintf("%.5f", x) : formatting decimal point, 

> e <- c(2.7182818284) # mathematical constant, the base of the natural logarithm

> 

> sprintf("%.0f", e)

[1] "3"

> sprintf("%.1f", e)

[1] "2.7"

> sprintf("%.2f", e)

[1] "2.72"

> sprintf("%.3f", e)

[1] "2.718"

> sprintf("%.5f", e)

[1] "2.71828"

> sprintf("%.10f", e)

[1] "2.7182818284"

> e <- c(2.7182818284) # mathematical constant, the base of the natural logarithm

> 

> sprintf("%1.1f", e)

[1] "2.7"

> sprintf("%2.1f", e)

[1] "2.7"

> sprintf("%3.1f", e)

[1] "2.7"

> sprintf("%5.1f", e)

[1] "  2.7"

> sprintf("%10.1f", e)

[1] "       2.7"

>  

#==========================

# Rssizing the plot

#==========================

install.packages("ggplot2")

library(ggplot2)

# X, Y coordinate and segments(A, B category) data set

my.df <- data.frame(XCoord = c(1, 1, 1, 2, 2, 2, 3, 3, 4, 4), 

                    YCoord = c(1, 2, 4, 1, 3, 4, 2, 4, 1, 4), 

                    Seg = c("A", "A", "A", "A", "B", "A", "B", "B", "B", "B"))

# Original plot

ggplot(my.df, aes(x=XCoord, y=YCoord, fill=Seg)) +

  geom_tile(colour="gray80") +

  scale_fill_manual(values = c("black", "white")) +

  ggtitle("Original Heatmap by X4*Y4 size")

# Resized plot using coord_fixed(ratio = number)

ggplot(my.df, aes(x=XCoord, y=YCoord, fill=Seg)) +

  geom_tile(colour="gray80") +

  scale_fill_manual(values = c("black", "white")) +

  coord_fixed(ratio = 2) +

  ggtitle("Heatmap : Resized X:Y axis size with 1:2 ratio")

# Resized plot using coord_fixed(ratio = number)

ggplot(my.df, aes(x=XCoord, y=YCoord, fill=Seg)) +

  geom_tile(colour="gray80") +

  scale_fill_manual(values = c("black", "white")) +

  coord_fixed(ratio = 0.5) +

  ggtitle("Heatmap : Resized X:Y axis size with 2:1 ratio")

#==============
# Image resize using EBImage package

# installing EBImage package
source("http://bioconductor.org/biocLite.R")
biocLite("EBImage")


# resizing image using EBImage package's resize() function

library("EBImage")

x <- readImage(system.file("images", "sample-color.png", package="EBImage"))


# width and height of the original image
dim(x)[1:2]


# scale to a specific width and height
y <- resize(x, w = 200, h = 100)


# scale by 50%; the height is determined automatically so that
# the aspect ratio is preserved
y <- resize(x, dim(x)[1]/2)


# show the scaled image
display(y)


# extract the pixel array
z <- imageData(y)


# or
z <- as.array(y)