Overview

Dataset statistics

Number of variables6
Number of observations781
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory38.3 KiB
Average record size in memory50.2 B

Variable types

Numeric2
Text3
DateTime1

Dataset

Description온라인 개인정보보호 포털 내 개인정보보호 뉴스 게시판 관련 정보입니다.
Author한국인터넷진흥원
URLhttps://www.data.go.kr/data/15070600/fileData.do

Alerts

인덱스 is highly overall correlated with 조회수High correlation
조회수 is highly overall correlated with 인덱스High correlation
인덱스 has unique valuesUnique

Reproduction

Analysis started2023-12-12 13:23:58.877184
Analysis finished2023-12-12 13:24:00.441866
Duration1.56 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

인덱스
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct781
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean430.01408
Minimum2
Maximum839
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.0 KiB
2023-12-12T22:24:00.532594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile46
Q1228
median437
Q3636
95-th percentile800
Maximum839
Range837
Interquartile range (IQR)408

Descriptive statistics

Standard deviation240.08991
Coefficient of variation (CV)0.55833033
Kurtosis-1.1724508
Mean430.01408
Median Absolute Deviation (MAD)204
Skewness-0.058273693
Sum335841
Variance57643.163
MonotonicityStrictly increasing
2023-12-12T22:24:00.708852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 1
 
0.1%
588 1
 
0.1%
564 1
 
0.1%
565 1
 
0.1%
566 1
 
0.1%
567 1
 
0.1%
568 1
 
0.1%
569 1
 
0.1%
570 1
 
0.1%
571 1
 
0.1%
Other values (771) 771
98.7%
ValueCountFrequency (%)
2 1
0.1%
5 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
11 1
0.1%
12 1
0.1%
13 1
0.1%
14 1
0.1%
16 1
0.1%
ValueCountFrequency (%)
839 1
0.1%
838 1
0.1%
837 1
0.1%
836 1
0.1%
835 1
0.1%
834 1
0.1%
833 1
0.1%
832 1
0.1%
831 1
0.1%
830 1
0.1%

제목
Text

Distinct779
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
2023-12-12T22:24:01.192798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length56
Median length45
Mean length24.309859
Min length4

Characters and Unicode

Total characters18986
Distinct characters760
Distinct categories13 ?
Distinct scripts4 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique777 ?
Unique (%)99.5%

Sample

1st row안드로이드 앱 47% 고객정보 유출
2nd row보안관리자 위장 해킹메일 주의보
3rd row스마트폰 공짜앱 통해 개인정보줄줄샌다
4th row사이트 클릭 순간 내 신상정보
5th row"웹사이트 접속순간, 당신은 노출된다"
ValueCountFrequency (%)
개인정보 211
 
4.7%
유출 85
 
1.9%
주민번호 36
 
0.8%
정보 35
 
0.8%
개인정보보호 34
 
0.8%
방통위 31
 
0.7%
구글 30
 
0.7%
고객정보 29
 
0.6%
수집 23
 
0.5%
인터넷 23
 
0.5%
Other values (2561) 3928
88.0%
2023-12-12T22:24:01.858187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3691
 
19.4%
676
 
3.6%
572
 
3.0%
478
 
2.5%
375
 
2.0%
233
 
1.2%
, 223
 
1.2%
210
 
1.1%
' 195
 
1.0%
186
 
1.0%
Other values (750) 12147
64.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13062
68.8%
Space Separator 3691
 
19.4%
Other Punctuation 959
 
5.1%
Decimal Number 547
 
2.9%
Uppercase Letter 391
 
2.1%
Modifier Symbol 86
 
0.5%
Final Punctuation 75
 
0.4%
Initial Punctuation 72
 
0.4%
Lowercase Letter 43
 
0.2%
Open Punctuation 18
 
0.1%
Other values (3) 42
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
676
 
5.2%
572
 
4.4%
478
 
3.7%
375
 
2.9%
233
 
1.8%
210
 
1.6%
186
 
1.4%
181
 
1.4%
175
 
1.3%
172
 
1.3%
Other values (673) 9804
75.1%
Uppercase Letter
ValueCountFrequency (%)
S 111
28.4%
N 45
11.5%
K 41
 
10.5%
I 34
 
8.7%
T 33
 
8.4%
A 26
 
6.6%
M 15
 
3.8%
C 14
 
3.6%
U 14
 
3.6%
E 9
 
2.3%
Other values (13) 49
12.5%
Lowercase Letter
ValueCountFrequency (%)
e 7
16.3%
o 6
14.0%
v 6
14.0%
r 4
9.3%
t 3
7.0%
i 3
7.0%
s 2
 
4.7%
g 2
 
4.7%
h 2
 
4.7%
b 2
 
4.7%
Other values (6) 6
14.0%
Other Punctuation
ValueCountFrequency (%)
, 223
23.3%
' 195
20.3%
. 137
14.3%
" 133
13.9%
130
13.6%
· 103
10.7%
% 23
 
2.4%
! 9
 
0.9%
2
 
0.2%
* 2
 
0.2%
Other values (2) 2
 
0.2%
Decimal Number
ValueCountFrequency (%)
0 163
29.8%
1 104
19.0%
2 73
13.3%
3 42
 
7.7%
5 39
 
7.1%
4 38
 
6.9%
8 25
 
4.6%
6 25
 
4.6%
7 25
 
4.6%
9 13
 
2.4%
Math Symbol
ValueCountFrequency (%)
5
35.7%
~ 4
28.6%
2
 
14.3%
+ 2
 
14.3%
1
 
7.1%
Final Punctuation
ValueCountFrequency (%)
50
66.7%
25
33.3%
Initial Punctuation
ValueCountFrequency (%)
47
65.3%
25
34.7%
Open Punctuation
ValueCountFrequency (%)
[ 12
66.7%
( 6
33.3%
Close Punctuation
ValueCountFrequency (%)
] 11
64.7%
) 6
35.3%
Space Separator
ValueCountFrequency (%)
3691
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 86
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13011
68.5%
Common 5490
28.9%
Latin 434
 
2.3%
Han 51
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
676
 
5.2%
572
 
4.4%
478
 
3.7%
375
 
2.9%
233
 
1.8%
210
 
1.6%
186
 
1.4%
181
 
1.4%
175
 
1.3%
172
 
1.3%
Other values (653) 9753
75.0%
Latin
ValueCountFrequency (%)
S 111
25.6%
N 45
10.4%
K 41
 
9.4%
I 34
 
7.8%
T 33
 
7.6%
A 26
 
6.0%
M 15
 
3.5%
C 14
 
3.2%
U 14
 
3.2%
E 9
 
2.1%
Other values (29) 92
21.2%
Common
ValueCountFrequency (%)
3691
67.2%
, 223
 
4.1%
' 195
 
3.6%
0 163
 
3.0%
. 137
 
2.5%
" 133
 
2.4%
130
 
2.4%
1 104
 
1.9%
· 103
 
1.9%
` 86
 
1.6%
Other values (28) 525
 
9.6%
Han
ValueCountFrequency (%)
24
47.1%
4
 
7.8%
2
 
3.9%
2
 
3.9%
2
 
3.9%
2
 
3.9%
2
 
3.9%
1
 
2.0%
1
 
2.0%
1
 
2.0%
Other values (10) 10
19.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13001
68.5%
ASCII 5533
29.1%
Punctuation 278
 
1.5%
None 105
 
0.6%
CJK 51
 
0.3%
Compat Jamo 10
 
0.1%
Arrows 7
 
< 0.1%
Math Operators 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3691
66.7%
, 223
 
4.0%
' 195
 
3.5%
0 163
 
2.9%
. 137
 
2.5%
" 133
 
2.4%
S 111
 
2.0%
1 104
 
1.9%
` 86
 
1.6%
2 73
 
1.3%
Other values (56) 617
 
11.2%
Hangul
ValueCountFrequency (%)
676
 
5.2%
572
 
4.4%
478
 
3.7%
375
 
2.9%
233
 
1.8%
210
 
1.6%
186
 
1.4%
181
 
1.4%
175
 
1.3%
172
 
1.3%
Other values (652) 9743
74.9%
Punctuation
ValueCountFrequency (%)
130
46.8%
50
 
18.0%
47
 
16.9%
25
 
9.0%
25
 
9.0%
1
 
0.4%
None
ValueCountFrequency (%)
· 103
98.1%
2
 
1.9%
CJK
ValueCountFrequency (%)
24
47.1%
4
 
7.8%
2
 
3.9%
2
 
3.9%
2
 
3.9%
2
 
3.9%
2
 
3.9%
1
 
2.0%
1
 
2.0%
1
 
2.0%
Other values (10) 10
19.6%
Compat Jamo
ValueCountFrequency (%)
10
100.0%
Arrows
ValueCountFrequency (%)
5
71.4%
2
 
28.6%
Math Operators
ValueCountFrequency (%)
1
100.0%

내용
Text

Distinct780
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
2023-12-12T22:24:02.146764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length297
Median length103
Mean length86.038412
Min length26

Characters and Unicode

Total characters67196
Distinct characters762
Distinct categories15 ?
Distinct scripts4 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique779 ?
Unique (%)99.7%

Sample

1st row[파이낸셜뉴스] 2010-07-31, 안드로이드앱 47% 고객정보 유출 ◎ 안드로이드' 스마트폰용 응용프로그램 중 절반 정도가 고객의 동의를 얻지 ...
2nd row[디지털타임스]2010-08-02, 보안관리자 위장 해킹메일 주의보 ◎ 정부기관을 대상으로정보보호 담당자 명의를 가장한 메일 유포, 민간...
3rd row[한국경제] 2010-08-02, 스마트폰 공짜앱 통해 개인정보줄줄샌다 ◎ 지난달 28일부터 1일까지 미국 라스베이거스에서 블랙햇과 데프콘이라는 세계적으로 가...
4th row[세계일보] 2010-08-02, 사이트 클릭 순간 내 신상정보"줄줄" ◎ 인터넷 사이트에 접속하는 순간 개인정보 수집 프로그램 설치로 개인정보 유출
5th row[한국일보] 2010-08-02, "웹사이트 접속순간, 당신은 노출된다" ◎ 인터넷 사이트 접속 시 개인정보 수집 프로그램 무단설치 ◎ 추적 프로그램은 ...
ValueCountFrequency (%)
703
 
9.0%
출처 467
 
6.0%
개인정보 132
 
1.7%
100
 
1.3%
67
 
0.9%
67
 
0.9%
유출 57
 
0.7%
전자신문 49
 
0.6%
48
 
0.6%
인터넷 34
 
0.4%
Other values (4123) 6114
78.0%
2023-12-12T22:24:02.630001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8089
 
12.0%
. 3485
 
5.2%
0 3052
 
4.5%
/ 2285
 
3.4%
1 2177
 
3.2%
t 2043
 
3.0%
e 1720
 
2.6%
2 1708
 
2.5%
w 1640
 
2.4%
n 1383
 
2.1%
Other values (752) 39614
59.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 19303
28.7%
Lowercase Letter 18102
26.9%
Decimal Number 10476
15.6%
Space Separator 8089
12.0%
Other Punctuation 7616
 
11.3%
Uppercase Letter 962
 
1.4%
Math Symbol 670
 
1.0%
Dash Punctuation 612
 
0.9%
Open Punctuation 402
 
0.6%
Close Punctuation 391
 
0.6%
Other values (5) 573
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
699
 
3.6%
677
 
3.5%
556
 
2.9%
546
 
2.8%
543
 
2.8%
517
 
2.7%
344
 
1.8%
304
 
1.6%
290
 
1.5%
242
 
1.3%
Other values (656) 14585
75.6%
Lowercase Letter
ValueCountFrequency (%)
t 2043
11.3%
e 1720
 
9.5%
w 1640
 
9.1%
n 1383
 
7.6%
o 1141
 
6.3%
h 1088
 
6.0%
i 1077
 
5.9%
s 999
 
5.5%
p 976
 
5.4%
c 961
 
5.3%
Other values (16) 5074
28.0%
Uppercase Letter
ValueCountFrequency (%)
S 187
19.4%
N 86
8.9%
A 85
8.8%
V 75
 
7.8%
D 67
 
7.0%
I 59
 
6.1%
C 54
 
5.6%
T 47
 
4.9%
K 46
 
4.8%
R 46
 
4.8%
Other values (15) 210
21.8%
Other Punctuation
ValueCountFrequency (%)
. 3485
45.8%
/ 2285
30.0%
: 1031
 
13.5%
, 451
 
5.9%
' 131
 
1.7%
" 108
 
1.4%
· 77
 
1.0%
26
 
0.3%
% 13
 
0.2%
! 5
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 3052
29.1%
1 2177
20.8%
2 1708
16.3%
3 673
 
6.4%
4 569
 
5.4%
7 487
 
4.6%
5 469
 
4.5%
8 468
 
4.5%
9 438
 
4.2%
6 435
 
4.2%
Math Symbol
ValueCountFrequency (%)
= 657
98.1%
| 6
 
0.9%
~ 3
 
0.4%
+ 2
 
0.3%
1
 
0.1%
1
 
0.1%
Open Punctuation
ValueCountFrequency (%)
[ 275
68.4%
( 81
 
20.1%
45
 
11.2%
1
 
0.2%
Close Punctuation
ValueCountFrequency (%)
] 275
70.3%
) 70
 
17.9%
45
 
11.5%
1
 
0.3%
Other Symbol
ValueCountFrequency (%)
169
97.7%
4
 
2.3%
Initial Punctuation
ValueCountFrequency (%)
35
79.5%
9
 
20.5%
Final Punctuation
ValueCountFrequency (%)
27
84.4%
5
 
15.6%
Space Separator
ValueCountFrequency (%)
8089
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 612
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 297
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28829
42.9%
Hangul 19285
28.7%
Latin 19064
28.4%
Han 18
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
699
 
3.6%
677
 
3.5%
556
 
2.9%
546
 
2.8%
543
 
2.8%
517
 
2.7%
344
 
1.8%
304
 
1.6%
290
 
1.5%
242
 
1.3%
Other values (644) 14567
75.5%
Latin
ValueCountFrequency (%)
t 2043
 
10.7%
e 1720
 
9.0%
w 1640
 
8.6%
n 1383
 
7.3%
o 1141
 
6.0%
h 1088
 
5.7%
i 1077
 
5.6%
s 999
 
5.2%
p 976
 
5.1%
c 961
 
5.0%
Other values (41) 6036
31.7%
Common
ValueCountFrequency (%)
8089
28.1%
. 3485
12.1%
0 3052
 
10.6%
/ 2285
 
7.9%
1 2177
 
7.6%
2 1708
 
5.9%
: 1031
 
3.6%
3 673
 
2.3%
= 657
 
2.3%
- 612
 
2.1%
Other values (35) 5060
17.6%
Han
ValueCountFrequency (%)
6
33.3%
2
 
11.1%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
Other values (2) 2
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47447
70.6%
Hangul 19273
28.7%
Geometric Shapes 173
 
0.3%
None 169
 
0.3%
Punctuation 102
 
0.2%
CJK 18
 
< 0.1%
Compat Jamo 12
 
< 0.1%
Arrows 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8089
17.0%
. 3485
 
7.3%
0 3052
 
6.4%
/ 2285
 
4.8%
1 2177
 
4.6%
t 2043
 
4.3%
e 1720
 
3.6%
2 1708
 
3.6%
w 1640
 
3.5%
n 1383
 
2.9%
Other values (72) 19865
41.9%
Hangul
ValueCountFrequency (%)
699
 
3.6%
677
 
3.5%
556
 
2.9%
546
 
2.8%
543
 
2.8%
517
 
2.7%
344
 
1.8%
304
 
1.6%
290
 
1.5%
242
 
1.3%
Other values (642) 14555
75.5%
Geometric Shapes
ValueCountFrequency (%)
169
97.7%
4
 
2.3%
None
ValueCountFrequency (%)
· 77
45.6%
45
26.6%
45
26.6%
1
 
0.6%
1
 
0.6%
Punctuation
ValueCountFrequency (%)
35
34.3%
27
26.5%
26
25.5%
9
 
8.8%
5
 
4.9%
Compat Jamo
ValueCountFrequency (%)
11
91.7%
1
 
8.3%
CJK
ValueCountFrequency (%)
6
33.3%
2
 
11.1%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
Other values (2) 2
 
11.1%
Arrows
ValueCountFrequency (%)
1
50.0%
1
50.0%
Distinct88
Distinct (%)11.3%
Missing1
Missing (%)0.1%
Memory size6.2 KiB
2023-12-12T22:24:02.877547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length4
Mean length4.5307692
Min length3

Characters and Unicode

Total characters3534
Distinct characters130
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)5.1%

Sample

1st row파이낸셜뉴스
2nd row디지털타임스
3rd row한국경제
4th row세계일보
5th row한국일보
ValueCountFrequency (%)
전자신문 101
 
12.8%
디지털타임스 72
 
9.1%
매일경제 45
 
5.7%
조선일보 41
 
5.2%
머니투데이 33
 
4.2%
연합뉴스 32
 
4.1%
한국경제 28
 
3.5%
서울경제 26
 
3.3%
파이낸셜뉴스 24
 
3.0%
경향신문 23
 
2.9%
Other values (81) 364
46.1%
2023-12-12T22:24:03.228508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
208
 
5.9%
181
 
5.1%
168
 
4.8%
167
 
4.7%
164
 
4.6%
155
 
4.4%
141
 
4.0%
105
 
3.0%
105
 
3.0%
102
 
2.9%
Other values (120) 2038
57.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3357
95.0%
Uppercase Letter 122
 
3.5%
Lowercase Letter 24
 
0.7%
Decimal Number 16
 
0.5%
Space Separator 9
 
0.3%
Other Punctuation 6
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
208
 
6.2%
181
 
5.4%
168
 
5.0%
167
 
5.0%
164
 
4.9%
155
 
4.6%
141
 
4.2%
105
 
3.1%
105
 
3.1%
102
 
3.0%
Other values (87) 1861
55.4%
Uppercase Letter
ValueCountFrequency (%)
S 22
18.0%
T 21
17.2%
N 18
14.8%
B 18
14.8%
Y 13
10.7%
K 9
7.4%
C 5
 
4.1%
I 5
 
4.1%
V 3
 
2.5%
E 2
 
1.6%
Other values (5) 6
 
4.9%
Lowercase Letter
ValueCountFrequency (%)
w 4
16.7%
o 4
16.7%
e 3
12.5%
r 3
12.5%
t 3
12.5%
h 2
8.3%
s 1
 
4.2%
a 1
 
4.2%
k 1
 
4.2%
b 1
 
4.2%
Decimal Number
ValueCountFrequency (%)
2 7
43.8%
4 7
43.8%
1 2
 
12.5%
Other Punctuation
ValueCountFrequency (%)
. 3
50.0%
/ 2
33.3%
: 1
 
16.7%
Space Separator
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3357
95.0%
Latin 146
 
4.1%
Common 31
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
208
 
6.2%
181
 
5.4%
168
 
5.0%
167
 
5.0%
164
 
4.9%
155
 
4.6%
141
 
4.2%
105
 
3.1%
105
 
3.1%
102
 
3.0%
Other values (87) 1861
55.4%
Latin
ValueCountFrequency (%)
S 22
15.1%
T 21
14.4%
N 18
12.3%
B 18
12.3%
Y 13
8.9%
K 9
 
6.2%
C 5
 
3.4%
I 5
 
3.4%
w 4
 
2.7%
o 4
 
2.7%
Other values (16) 27
18.5%
Common
ValueCountFrequency (%)
9
29.0%
2 7
22.6%
4 7
22.6%
. 3
 
9.7%
/ 2
 
6.5%
1 2
 
6.5%
: 1
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3357
95.0%
ASCII 177
 
5.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
208
 
6.2%
181
 
5.4%
168
 
5.0%
167
 
5.0%
164
 
4.9%
155
 
4.6%
141
 
4.2%
105
 
3.1%
105
 
3.1%
102
 
3.0%
Other values (87) 1861
55.4%
ASCII
ValueCountFrequency (%)
S 22
12.4%
T 21
11.9%
N 18
 
10.2%
B 18
 
10.2%
Y 13
 
7.3%
9
 
5.1%
K 9
 
5.1%
2 7
 
4.0%
4 7
 
4.0%
C 5
 
2.8%
Other values (23) 48
27.1%
Distinct776
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
Minimum2010-08-31 11:32:00
Maximum2014-06-12 09:07:00
2023-12-12T22:24:03.381011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:03.516567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

조회수
Real number (ℝ)

HIGH CORRELATION 

Distinct391
Distinct (%)50.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1225.8873
Minimum254
Maximum2053
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.0 KiB
2023-12-12T22:24:03.694547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum254
5-th percentile576
Q11196
median1280
Q31344
95-th percentile1458
Maximum2053
Range1799
Interquartile range (IQR)148

Descriptive statistics

Standard deviation240.04868
Coefficient of variation (CV)0.19581626
Kurtosis5.4438264
Mean1225.8873
Median Absolute Deviation (MAD)69
Skewness-2.1474029
Sum957418
Variance57623.367
MonotonicityNot monotonic
2023-12-12T22:24:03.907573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1260 9
 
1.2%
1268 9
 
1.2%
1322 8
 
1.0%
1304 7
 
0.9%
1307 7
 
0.9%
1331 6
 
0.8%
1344 6
 
0.8%
1279 6
 
0.8%
1311 6
 
0.8%
1277 6
 
0.8%
Other values (381) 711
91.0%
ValueCountFrequency (%)
254 1
0.1%
289 1
0.1%
294 1
0.1%
298 1
0.1%
302 2
0.3%
303 1
0.1%
313 1
0.1%
328 1
0.1%
330 2
0.3%
334 1
0.1%
ValueCountFrequency (%)
2053 1
0.1%
1806 1
0.1%
1736 1
0.1%
1734 1
0.1%
1721 1
0.1%
1641 1
0.1%
1605 1
0.1%
1577 1
0.1%
1553 1
0.1%
1541 1
0.1%

Interactions

2023-12-12T22:23:59.956315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:59.751250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:24:00.072381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:23:59.844454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:24:04.017588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인덱스자료출처조회수
인덱스1.0000.5800.825
자료출처0.5801.0000.000
조회수0.8250.0001.000
2023-12-12T22:24:04.115609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인덱스조회수
인덱스1.000-0.648
조회수-0.6481.000

Missing values

2023-12-12T22:24:00.253317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:24:00.383825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

인덱스제목내용자료출처등록일조회수
02안드로이드 앱 47% 고객정보 유출[파이낸셜뉴스] 2010-07-31, 안드로이드앱 47% 고객정보 유출 ◎ 안드로이드' 스마트폰용 응용프로그램 중 절반 정도가 고객의 동의를 얻지 ...파이낸셜뉴스2010-08-31 11:321404
15보안관리자 위장 해킹메일 주의보[디지털타임스]2010-08-02, 보안관리자 위장 해킹메일 주의보 ◎ 정부기관을 대상으로정보보호 담당자 명의를 가장한 메일 유포, 민간...디지털타임스2010-08-31 15:081469
27스마트폰 공짜앱 통해 개인정보줄줄샌다[한국경제] 2010-08-02, 스마트폰 공짜앱 통해 개인정보줄줄샌다 ◎ 지난달 28일부터 1일까지 미국 라스베이거스에서 블랙햇과 데프콘이라는 세계적으로 가...한국경제2010-08-31 15:281452
38사이트 클릭 순간 내 신상정보[세계일보] 2010-08-02, 사이트 클릭 순간 내 신상정보"줄줄" ◎ 인터넷 사이트에 접속하는 순간 개인정보 수집 프로그램 설치로 개인정보 유출세계일보2010-08-31 15:471473
49"웹사이트 접속순간, 당신은 노출된다"[한국일보] 2010-08-02, "웹사이트 접속순간, 당신은 노출된다" ◎ 인터넷 사이트 접속 시 개인정보 수집 프로그램 무단설치 ◎ 추적 프로그램은 ...한국일보2010-08-31 15:571447
511"사용자 인식개선이 보안강화 지름길"[전자신문] 2010-08-02, "사용자 인식개선이 보안강화 지름길" ◎ 사용자들의보안 인식에 대한 공감대가 형성되어 있지 않기때문에 보안 이슈가 터지면 보안...전자신문2010-08-31 16:181500
612"동의없이 제3자 제공"최다[문화일보] 2010-08-02, "동의없이 제3자 제공"최다 ◎ 2000년 2035건에서 2009년 3만5167건으로 급증 ◎ 사업자들의 기술적·관리적 조치 미흡문화일보2010-08-31 16:291439
713페이스북, 1억7천만명 개인정보 샜다[한겨레신문] 2010-08-03, 페이스북, 1억7천만명 개인정보 샜다 ◎ 페이스북 온라인 디렉토리에서 사용자 정보 추출 ◎상업적 의도를 가진 업...한겨레신문2010-08-31 16:331480
814m오피스 정보유출 차단 방안에 골몰[전자신문] 2010-08-03, m오피스 정보유출차단 방안에 골몰 ◎ 스마트폰을 이용한 기밀 누출을 막기위한 대책 ◎ 모바일 오피스와 스마트폰 ...전자신문2010-08-31 16:401458
916떠다니는 개인정보..황당한 캠퍼스[파이낸셜뉴스] 2010-08-04, 떠다니는 개인정보..황당한 캠퍼스 ◎ 개인정보보호에 대한 인식 부족 ◎ 개인정보보호에 대한 인식 개선 및 보안 시...파이낸셜뉴스2010-08-31 17:251541
인덱스제목내용자료출처등록일조회수
771830화장품업체 스킨푸드도 개인정보 55만명 유출…출처 : http://www.dt.co.kr/contents.htmlarticle_no=2014041702019960800002 화장품브랜드 스킨푸드도 회원 개...디지털타임즈2014-04-18 9:48874
772831내 스마트폰 개인정보 5분이면 다 턴다출처 : http://view.asiae.co.kr/news/view.htmidxno=2014042607572602892 불과 5분. 스마...아시아경제2014-04-28 9:051553
773832“매시간 9만건의 데이터가 유출된다”…세이프넷, 데이터 유출 통계자료 발표출처 : http://www.ddaily.co.kr/news/article.htmlno=118091 전세계에서 매시간마다 9만건 이상의 개인정보, 민감정보...디지털데일리2014-05-07 17:251287
774833개인정보 유출 시, 이용자 최대 300만원 받는다출처 : http://www.newsis.com/ar_detail/view.htmlar_id=NISX20140508_0012904535cID=10402pID=10400 ...뉴시스2014-05-09 9:301374
775834경찰, 개인정보 유출단속 100일 특별작전에서 회수한 개인정보만 4억여건출처 : http://news.khan.co.kr/kh_news/khan_art_view.htmlartid=201405120600011code=940202 경찰청이 지난...경향신문2014-05-12 9:101344
776835인터넷서 ‘잊혀질 권리’ 첫 인정출처 : http://news.donga.com/3/all/20140514/63453443/1 유럽 최고법원이 인터넷에서 개인의 ‘잊혀질 권리(right to be forg...동아일보2014-05-14 9:081293
777836정보유출 심각, ‘개인정보 스스로 지키자’ 캠페인출처 : http://www.ebn.co.kr/news/view/682391 정보유출 문제가 심각한 가운데 ‘개인정보를 스스로 지키자’는 캠페인이 정...EBN2014-05-19 9:171085
778837개인정보 유출되면 한 달 간 신용조회 막을 수 있다출처 : http://biz.heraldcorp.com/view.phpud=20140523000452 오는 7월부터 개인정보 유출 피해 고객들은 신용조회 회...해럴드경제2014-05-23 16:43812
779838주민등록번호 3000만개가 10대의 손안에츨처 : http://www.hani.co.kr/arti/society/society_general/641230.html 주민등록번호 6000만개를 수집해 이를 스...한겨례2014-06-09 8:57355
780839카드사 기준없이 제각각 정보 수집…당국 수수방관출처 : http://www.segye.com/content/html/2014/06/11/20140611005746.html 일부 카드사들이 수년 전부터 고객 컴퓨터·스마트폰의 고유번호를 수집하고 있음에도 금융당국은 현황 파악과 보안 대책 마련을 소홀히 했던 것으로 드러났다. 당국이 지난달 ‘개인정보 유출 재발방지 종합대책’을 발표하면서 “금융회사가 최소한의 정보만 수집하도록 개편안을 마련하겠다”고 했지만, 맥 주소 등의 수집에 대해서는 전혀 논의조차 하지 않은 것이다. = 이하 기사내용은 출처의 링크를 클릭해 주세요. =세계일보2014-06-12 9:07302