Overview

Dataset statistics

Number of variables14
Number of observations542
Missing cells2603
Missing cells (%)34.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory61.0 KiB
Average record size in memory115.2 B

Variable types

Text9
Numeric3
Categorical2

Dataset

Description경상남도 사천시 문화관광홈페이지 전시내용 테이블 자료(제목,조회수,등록일, 주소,위도, 경도 등)에 대한 자료(중국어) 입니다.
Author경상남도 사천시
URLhttps://www.data.go.kr/data/15084118/fileData.do

Alerts

계절 is highly imbalanced (87.1%)Imbalance
주소 has 10 (1.8%) missing valuesMissing
위치 has 454 (83.8%) missing valuesMissing
시간 has 384 (70.8%) missing valuesMissing
홈페이지 has 498 (91.9%) missing valuesMissing
지도 주소 has 29 (5.4%) missing valuesMissing
동영상 링크 has 532 (98.2%) missing valuesMissing
vr 링크 has 526 (97.0%) missing valuesMissing
값1 has 102 (18.8%) missing valuesMissing
위도 has 34 (6.3%) missing valuesMissing
경도 has 34 (6.3%) missing valuesMissing

Reproduction

Analysis started2023-12-12 04:29:59.737527
Analysis finished2023-12-12 04:30:02.893936
Duration3.16 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

제목
Text

Distinct537
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
2023-12-12T13:30:03.094608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length22
Mean length9.3616236
Min length1

Characters and Unicode

Total characters5074
Distinct characters514
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique532 ?
Unique (%)98.2%

Sample

1st row卧龙山
2nd row凤鸣山
3rd row角山
4th row闲丽水道
5th row南逸台海水浴场
ValueCountFrequency (%)
11
 
1.9%
beuimotel 2
 
0.3%
泗川大桥 2
 
0.3%
체육관 2
 
0.3%
多率寺 2
 
0.3%
emmotel 2
 
0.3%
三千浦 2
 
0.3%
민박 2
 
0.3%
大芳镇掘港 2
 
0.3%
泗川船津里城 2
 
0.3%
Other values (556) 556
95.0%
2023-12-12T13:30:03.547991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 417
 
8.2%
o 406
 
8.0%
n 325
 
6.4%
a 297
 
5.9%
g 218
 
4.3%
i 185
 
3.6%
t 170
 
3.4%
m 158
 
3.1%
l 153
 
3.0%
j 123
 
2.4%
Other values (504) 2622
51.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3168
62.4%
Other Letter 1492
29.4%
Uppercase Letter 324
 
6.4%
Space Separator 43
 
0.8%
Decimal Number 17
 
0.3%
Dash Punctuation 11
 
0.2%
Close Punctuation 8
 
0.2%
Open Punctuation 8
 
0.2%
Other Punctuation 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
식 34
 
2.3%
당 29
 
1.9%
집 21
 
1.4%
가 21
 
1.4%
장 20
 
1.3%
산 20
 
1.3%
정 17
 
1.1%
구 16
 
1.1%
사 15
 
1.0%
川 14
 
0.9%
Other values (449) 1285
86.1%
Lowercase Letter
ValueCountFrequency (%)
e 417
13.2%
o 406
12.8%
n 325
10.3%
a 297
9.4%
g 218
 
6.9%
i 185
 
5.8%
t 170
 
5.4%
m 158
 
5.0%
l 153
 
4.8%
j 123
 
3.9%
Other values (11) 716
22.6%
Uppercase Letter
ValueCountFrequency (%)
S 59
18.2%
G 32
 
9.9%
D 27
 
8.3%
J 26
 
8.0%
H 25
 
7.7%
B 22
 
6.8%
M 14
 
4.3%
C 13
 
4.0%
P 13
 
4.0%
R 12
 
3.7%
Other values (10) 81
25.0%
Decimal Number
ValueCountFrequency (%)
0 4
23.5%
7 3
17.6%
3 3
17.6%
4 2
11.8%
8 2
11.8%
9 1
 
5.9%
1 1
 
5.9%
2 1
 
5.9%
Other Punctuation
ValueCountFrequency (%)
& 2
66.7%
. 1
33.3%
Space Separator
ValueCountFrequency (%)
43
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3492
68.8%
Hangul 1099
 
21.7%
Han 393
 
7.7%
Common 90
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
식 34
 
3.1%
당 29
 
2.6%
집 21
 
1.9%
가 21
 
1.9%
장 20
 
1.8%
산 20
 
1.8%
정 17
 
1.5%
구 16
 
1.5%
사 15
 
1.4%
해 14
 
1.3%
Other values (262) 892
81.2%
Han
ValueCountFrequency (%)
川 14
 
3.6%
泗 13
 
3.3%
村 12
 
3.1%
园 12
 
3.1%
大 11
 
2.8%
场 11
 
2.8%
山 10
 
2.5%
公 10
 
2.5%
体 9
 
2.3%
验 9
 
2.3%
Other values (177) 282
71.8%
Latin
ValueCountFrequency (%)
e 417
11.9%
o 406
 
11.6%
n 325
 
9.3%
a 297
 
8.5%
g 218
 
6.2%
i 185
 
5.3%
t 170
 
4.9%
m 158
 
4.5%
l 153
 
4.4%
j 123
 
3.5%
Other values (31) 1040
29.8%
Common
ValueCountFrequency (%)
43
47.8%
- 11
 
12.2%
) 8
 
8.9%
( 8
 
8.9%
0 4
 
4.4%
7 3
 
3.3%
3 3
 
3.3%
4 2
 
2.2%
8 2
 
2.2%
& 2
 
2.2%
Other values (4) 4
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3582
70.6%
Hangul 1099
 
21.7%
CJK 393
 
7.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 417
 
11.6%
o 406
 
11.3%
n 325
 
9.1%
a 297
 
8.3%
g 218
 
6.1%
i 185
 
5.2%
t 170
 
4.7%
m 158
 
4.4%
l 153
 
4.3%
j 123
 
3.4%
Other values (45) 1130
31.5%
Hangul
ValueCountFrequency (%)
식 34
 
3.1%
당 29
 
2.6%
집 21
 
1.9%
가 21
 
1.9%
장 20
 
1.8%
산 20
 
1.8%
정 17
 
1.5%
구 16
 
1.5%
사 15
 
1.4%
해 14
 
1.3%
Other values (262) 892
81.2%
CJK
ValueCountFrequency (%)
川 14
 
3.6%
泗 13
 
3.3%
村 12
 
3.1%
园 12
 
3.1%
大 11
 
2.8%
场 11
 
2.8%
山 10
 
2.5%
公 10
 
2.5%
体 9
 
2.3%
验 9
 
2.3%
Other values (177) 282
71.8%

조회수
Real number (ℝ)

Distinct179
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean151.1107
Minimum1
Maximum1581
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2023-12-12T13:30:03.723074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q19
median18
Q3200.75
95-th percentile825
Maximum1581
Range1580
Interquartile range (IQR)191.75

Descriptive statistics

Standard deviation264.68936
Coefficient of variation (CV)1.7516255
Kurtosis7.3151076
Mean151.1107
Median Absolute Deviation (MAD)15
Skewness2.6562605
Sum81902
Variance70060.457
MonotonicityNot monotonic
2023-12-12T13:30:03.901738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 27
 
5.0%
2 24
 
4.4%
3 21
 
3.9%
12 20
 
3.7%
9 19
 
3.5%
4 18
 
3.3%
11 18
 
3.3%
8 16
 
3.0%
5 16
 
3.0%
1 14
 
2.6%
Other values (169) 349
64.4%
ValueCountFrequency (%)
1 14
2.6%
2 24
4.4%
3 21
3.9%
4 18
3.3%
5 16
3.0%
6 12
2.2%
7 14
2.6%
8 16
3.0%
9 19
3.5%
10 27
5.0%
ValueCountFrequency (%)
1581 1
0.2%
1409 1
0.2%
1355 1
0.2%
1308 1
0.2%
1253 1
0.2%
1217 1
0.2%
1183 1
0.2%
1182 1
0.2%
1134 1
0.2%
1114 1
0.2%

등록일
Categorical

Distinct21
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
2013-06-10
162 
2013-05-02
76 
2013-06-05
66 
2013-05-06
43 
2013-06-17
42 
Other values (16)
153 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row2013-04-30
2nd row2013-04-30
3rd row2013-04-30
4th row2013-04-30
5th row2013-04-30

Common Values

ValueCountFrequency (%)
2013-06-10 162
29.9%
2013-05-02 76
14.0%
2013-06-05 66
12.2%
2013-05-06 43
 
7.9%
2013-06-17 42
 
7.7%
2013-05-08 41
 
7.6%
2013-06-04 22
 
4.1%
2013-04-30 18
 
3.3%
2013-05-01 16
 
3.0%
2013-05-10 15
 
2.8%
Other values (11) 41
 
7.6%

Length

2023-12-12T13:30:04.088982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2013-06-10 162
29.9%
2013-05-02 76
14.0%
2013-06-05 66
12.2%
2013-05-06 43
 
7.9%
2013-06-17 42
 
7.7%
2013-05-08 41
 
7.6%
2013-06-04 22
 
4.1%
2013-04-30 18
 
3.3%
2013-05-01 16
 
3.0%
2013-05-10 15
 
2.8%
Other values (11) 41
 
7.6%

주소
Text

MISSING 

Distinct507
Distinct (%)95.3%
Missing10
Missing (%)1.8%
Memory size4.4 KiB
2023-12-12T13:30:04.405122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length22
Mean length14.291353
Min length6

Characters and Unicode

Total characters7603
Distinct characters141
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique495 ?
Unique (%)93.0%

Sample

1st row노룡동 1번지
2nd row곤명면 용산리 86일원
3rd row사천시 동림동 190일원
4th row늑도동 477 일원(초양휴게소)
5th row향촌동 710 일원
ValueCountFrequency (%)
사천읍 124
 
7.0%
용현면 49
 
2.8%
동금동 49
 
2.8%
수석리 49
 
2.8%
서금동 45
 
2.5%
벌리동 42
 
2.4%
1호 41
 
2.3%
곤명면 35
 
2.0%
4호 34
 
1.9%
3호 32
 
1.8%
Other values (502) 1270
71.8%
2023-12-12T13:30:04.900835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1566
20.6%
지 413
 
5.4%
1 406
 
5.3%
번 400
 
5.3%
동 351
 
4.6%
호 343
 
4.5%
리 315
 
4.1%
4 284
 
3.7%
2 251
 
3.3%
3 237
 
3.1%
Other values (131) 3037
39.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3821
50.3%
Decimal Number 2072
27.3%
Space Separator 1566
20.6%
Dash Punctuation 80
 
1.1%
Open Punctuation 23
 
0.3%
Close Punctuation 23
 
0.3%
Other Punctuation 16
 
0.2%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 413
 
10.8%
번 400
 
10.5%
동 351
 
9.2%
호 343
 
9.0%
리 315
 
8.2%
사 191
 
5.0%
천 161
 
4.2%
면 155
 
4.1%
금 128
 
3.3%
읍 126
 
3.3%
Other values (114) 1238
32.4%
Decimal Number
ValueCountFrequency (%)
1 406
19.6%
4 284
13.7%
2 251
12.1%
3 237
11.4%
6 198
9.6%
8 161
 
7.8%
5 156
 
7.5%
7 146
 
7.0%
9 134
 
6.5%
0 99
 
4.8%
Other Punctuation
ValueCountFrequency (%)
, 15
93.8%
/ 1
 
6.2%
Space Separator
ValueCountFrequency (%)
1566
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 80
100.0%
Open Punctuation
ValueCountFrequency (%)
( 23
100.0%
Close Punctuation
ValueCountFrequency (%)
) 23
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3821
50.3%
Common 3782
49.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 413
 
10.8%
번 400
 
10.5%
동 351
 
9.2%
호 343
 
9.0%
리 315
 
8.2%
사 191
 
5.0%
천 161
 
4.2%
면 155
 
4.1%
금 128
 
3.3%
읍 126
 
3.3%
Other values (114) 1238
32.4%
Common
ValueCountFrequency (%)
1566
41.4%
1 406
 
10.7%
4 284
 
7.5%
2 251
 
6.6%
3 237
 
6.3%
6 198
 
5.2%
8 161
 
4.3%
5 156
 
4.1%
7 146
 
3.9%
9 134
 
3.5%
Other values (7) 243
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3821
50.3%
ASCII 3782
49.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1566
41.4%
1 406
 
10.7%
4 284
 
7.5%
2 251
 
6.6%
3 237
 
6.3%
6 198
 
5.2%
8 161
 
4.3%
5 156
 
4.1%
7 146
 
3.9%
9 134
 
3.5%
Other values (7) 243
 
6.4%
Hangul
ValueCountFrequency (%)
지 413
 
10.8%
번 400
 
10.5%
동 351
 
9.2%
호 343
 
9.0%
리 315
 
8.2%
사 191
 
5.0%
천 161
 
4.2%
면 155
 
4.1%
금 128
 
3.3%
읍 126
 
3.3%
Other values (114) 1238
32.4%

위치
Text

MISSING 

Distinct81
Distinct (%)92.0%
Missing454
Missing (%)83.8%
Memory size4.4 KiB
2023-12-12T13:30:05.207593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length19
Mean length13.931818
Min length4

Characters and Unicode

Total characters1226
Distinct characters128
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique75 ?
Unique (%)85.2%

Sample

1st row사천시 사남면, 용현면, 벌용동, 남양동
2nd row사천공항에서 22km
3rd row사천공항에서 18Km
4th row삼천포항 일원
5th row삼천포항에서 동쪽으로 3.5㎞
ValueCountFrequency (%)
사천ic에서 30
 
12.7%
삼천포항 14
 
5.9%
곤양ic에서 11
 
4.7%
방향으로 10
 
4.2%
사남면 7
 
3.0%
일원 7
 
3.0%
사천시 7
 
3.0%
22km 5
 
2.1%
사천공항에서 4
 
1.7%
10km 4
 
1.7%
Other values (101) 137
58.1%
2023-12-12T13:30:05.740442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
192
 
15.7%
천 72
 
5.9%
서 61
 
5.0%
사 57
 
4.6%
에 56
 
4.6%
m 53
 
4.3%
C 42
 
3.4%
I 42
 
3.4%
k 39
 
3.2%
2 27
 
2.2%
Other values (118) 585
47.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 708
57.7%
Space Separator 192
 
15.7%
Decimal Number 100
 
8.2%
Uppercase Letter 97
 
7.9%
Lowercase Letter 92
 
7.5%
Other Punctuation 29
 
2.4%
Close Punctuation 3
 
0.2%
Open Punctuation 3
 
0.2%
Other Symbol 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 72
 
10.2%
서 61
 
8.6%
사 57
 
8.1%
에 56
 
7.9%
포 25
 
3.5%
항 24
 
3.4%
삼 22
 
3.1%
동 21
 
3.0%
곤 19
 
2.7%
양 19
 
2.7%
Other values (97) 332
46.9%
Decimal Number
ValueCountFrequency (%)
2 27
27.0%
1 17
17.0%
5 15
15.0%
0 11
11.0%
3 9
 
9.0%
6 9
 
9.0%
4 7
 
7.0%
9 2
 
2.0%
7 2
 
2.0%
8 1
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
C 42
43.3%
I 42
43.3%
K 13
 
13.4%
Lowercase Letter
ValueCountFrequency (%)
m 53
57.6%
k 39
42.4%
Other Punctuation
ValueCountFrequency (%)
, 19
65.5%
. 10
34.5%
Space Separator
ValueCountFrequency (%)
192
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Other Symbol
ValueCountFrequency (%)
㎞ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 708
57.7%
Common 329
26.8%
Latin 189
 
15.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 72
 
10.2%
서 61
 
8.6%
사 57
 
8.1%
에 56
 
7.9%
포 25
 
3.5%
항 24
 
3.4%
삼 22
 
3.1%
동 21
 
3.0%
곤 19
 
2.7%
양 19
 
2.7%
Other values (97) 332
46.9%
Common
ValueCountFrequency (%)
192
58.4%
2 27
 
8.2%
, 19
 
5.8%
1 17
 
5.2%
5 15
 
4.6%
0 11
 
3.3%
. 10
 
3.0%
3 9
 
2.7%
6 9
 
2.7%
4 7
 
2.1%
Other values (6) 13
 
4.0%
Latin
ValueCountFrequency (%)
m 53
28.0%
C 42
22.2%
I 42
22.2%
k 39
20.6%
K 13
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 708
57.7%
ASCII 516
42.1%
CJK Compat 2
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
192
37.2%
m 53
 
10.3%
C 42
 
8.1%
I 42
 
8.1%
k 39
 
7.6%
2 27
 
5.2%
, 19
 
3.7%
1 17
 
3.3%
5 15
 
2.9%
K 13
 
2.5%
Other values (10) 57
 
11.0%
Hangul
ValueCountFrequency (%)
천 72
 
10.2%
서 61
 
8.6%
사 57
 
8.1%
에 56
 
7.9%
포 25
 
3.5%
항 24
 
3.4%
삼 22
 
3.1%
동 21
 
3.0%
곤 19
 
2.7%
양 19
 
2.7%
Other values (97) 332
46.9%
CJK Compat
ValueCountFrequency (%)
㎞ 2
100.0%

시간
Text

MISSING 

Distinct54
Distinct (%)34.2%
Missing384
Missing (%)70.8%
Memory size4.4 KiB
2023-12-12T13:30:05.963566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length12.886076
Min length3

Characters and Unicode

Total characters2036
Distinct characters19
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)22.8%

Sample

1st row09:00 ~ 22:00
2nd row10:00 ~ 22:00
3rd row10:00 ~ 22:00
4th row10:00 ~ 22:00
5th row10:00 ~ 22:00
ValueCountFrequency (%)
154
33.1%
22:00 90
19.4%
10:00 61
 
13.1%
11:00 36
 
7.7%
21:00 26
 
5.6%
09:00 18
 
3.9%
20:00 8
 
1.7%
12:00 7
 
1.5%
20:30 7
 
1.5%
09:30 6
 
1.3%
Other values (26) 52
 
11.2%
2023-12-12T13:30:06.317135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 701
34.4%
313
15.4%
: 307
15.1%
2 250
 
12.3%
1 201
 
9.9%
~ 158
 
7.8%
3 38
 
1.9%
9 24
 
1.2%
시 9
 
0.4%
7 7
 
0.3%
Other values (9) 28
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1244
61.1%
Space Separator 313
 
15.4%
Other Punctuation 308
 
15.1%
Math Symbol 158
 
7.8%
Other Letter 13
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 701
56.4%
2 250
 
20.1%
1 201
 
16.2%
3 38
 
3.1%
9 24
 
1.9%
7 7
 
0.6%
6 7
 
0.6%
8 7
 
0.6%
4 6
 
0.5%
5 3
 
0.2%
Other Letter
ValueCountFrequency (%)
시 9
69.2%
오 1
 
7.7%
후 1
 
7.7%
까 1
 
7.7%
지 1
 
7.7%
Other Punctuation
ValueCountFrequency (%)
: 307
99.7%
, 1
 
0.3%
Space Separator
ValueCountFrequency (%)
313
100.0%
Math Symbol
ValueCountFrequency (%)
~ 158
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2023
99.4%
Hangul 13
 
0.6%

Most frequent character per script

Common
ValueCountFrequency (%)
0 701
34.7%
313
15.5%
: 307
15.2%
2 250
 
12.4%
1 201
 
9.9%
~ 158
 
7.8%
3 38
 
1.9%
9 24
 
1.2%
7 7
 
0.3%
6 7
 
0.3%
Other values (4) 17
 
0.8%
Hangul
ValueCountFrequency (%)
시 9
69.2%
오 1
 
7.7%
후 1
 
7.7%
까 1
 
7.7%
지 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2023
99.4%
Hangul 13
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 701
34.7%
313
15.5%
: 307
15.2%
2 250
 
12.4%
1 201
 
9.9%
~ 158
 
7.8%
3 38
 
1.9%
9 24
 
1.2%
7 7
 
0.3%
6 7
 
0.3%
Other values (4) 17
 
0.8%
Hangul
ValueCountFrequency (%)
시 9
69.2%
오 1
 
7.7%
후 1
 
7.7%
까 1
 
7.7%
지 1
 
7.7%

홈페이지
Text

MISSING 

Distinct28
Distinct (%)63.6%
Missing498
Missing (%)91.9%
Memory size4.4 KiB
2023-12-12T13:30:06.589724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length35
Mean length24.818182
Min length12

Characters and Unicode

Total characters1092
Distinct characters44
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)61.4%

Sample

1st rowhttp://bekchunsa.org/
2nd row서동 346번지 26호
3rd rowhttp://www.namiltte.com
4th rowhttp://blog.naver.com/guswn7262/50025659189
5th rowhttp://myhome.naver.com/kwonwoop
ValueCountFrequency (%)
http://www.4000mall.com 18
39.1%
http://www.namiltte.com 2
 
4.3%
http://www.seapensun.net 1
 
2.2%
http://damaek.seantour.org 1
 
2.2%
http://blog.naver.com/a0102433 1
 
2.2%
http://bitogaza.com 1
 
2.2%
http://sacheonart.co.kr 1
 
2.2%
http://www.dajayeon.com 1
 
2.2%
http://www.parkjaesam.com 1
 
2.2%
http://cafe.daum.net/jinjuwinds 1
 
2.2%
Other values (18) 18
39.1%
2023-12-12T13:30:07.026949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 100
 
9.2%
w 98
 
9.0%
t 95
 
8.7%
. 88
 
8.1%
0 63
 
5.8%
m 60
 
5.5%
o 59
 
5.4%
a 54
 
4.9%
p 48
 
4.4%
h 48
 
4.4%
Other values (34) 379
34.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 716
65.6%
Other Punctuation 230
 
21.1%
Decimal Number 111
 
10.2%
Space Separator 30
 
2.7%
Other Letter 5
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 98
13.7%
t 95
13.3%
m 60
8.4%
o 59
8.2%
a 54
 
7.5%
p 48
 
6.7%
h 48
 
6.7%
l 44
 
6.1%
c 39
 
5.4%
e 29
 
4.1%
Other values (15) 142
19.8%
Decimal Number
ValueCountFrequency (%)
0 63
56.8%
4 22
 
19.8%
2 6
 
5.4%
6 5
 
4.5%
3 4
 
3.6%
5 4
 
3.6%
1 3
 
2.7%
9 2
 
1.8%
8 1
 
0.9%
7 1
 
0.9%
Other Letter
ValueCountFrequency (%)
서 1
20.0%
호 1
20.0%
지 1
20.0%
번 1
20.0%
동 1
20.0%
Other Punctuation
ValueCountFrequency (%)
/ 100
43.5%
. 88
38.3%
: 42
18.3%
Space Separator
ValueCountFrequency (%)
30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 716
65.6%
Common 371
34.0%
Hangul 5
 
0.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 98
13.7%
t 95
13.3%
m 60
8.4%
o 59
8.2%
a 54
 
7.5%
p 48
 
6.7%
h 48
 
6.7%
l 44
 
6.1%
c 39
 
5.4%
e 29
 
4.1%
Other values (15) 142
19.8%
Common
ValueCountFrequency (%)
/ 100
27.0%
. 88
23.7%
0 63
17.0%
: 42
11.3%
30
 
8.1%
4 22
 
5.9%
2 6
 
1.6%
6 5
 
1.3%
3 4
 
1.1%
5 4
 
1.1%
Other values (4) 7
 
1.9%
Hangul
ValueCountFrequency (%)
서 1
20.0%
호 1
20.0%
지 1
20.0%
번 1
20.0%
동 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1087
99.5%
Hangul 5
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 100
 
9.2%
w 98
 
9.0%
t 95
 
8.7%
. 88
 
8.1%
0 63
 
5.8%
m 60
 
5.5%
o 59
 
5.4%
a 54
 
5.0%
p 48
 
4.4%
h 48
 
4.4%
Other values (29) 374
34.4%
Hangul
ValueCountFrequency (%)
서 1
20.0%
호 1
20.0%
지 1
20.0%
번 1
20.0%
동 1
20.0%

지도 주소
Text

MISSING 

Distinct495
Distinct (%)96.5%
Missing29
Missing (%)5.4%
Memory size4.4 KiB
2023-12-12T13:30:07.301102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length21
Mean length15.795322
Min length3

Characters and Unicode

Total characters8103
Distinct characters131
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique481 ?
Unique (%)93.8%

Sample

1st row노룡동 1번지
2nd row사천시 곤명면 용산리 86
3rd row사천시 동림동 190
4th row사천시 늑도동 477
5th row향촌동 710 일원
ValueCountFrequency (%)
사천시 306
 
15.9%
사천읍 122
 
6.4%
동금동 49
 
2.6%
수석리 48
 
2.5%
서금동 45
 
2.3%
벌리동 42
 
2.2%
1호 41
 
2.1%
용현면 38
 
2.0%
곤명면 34
 
1.8%
4호 33
 
1.7%
Other values (464) 1162
60.5%
2023-12-12T13:30:07.702740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1702
21.0%
사 470
 
5.8%
천 441
 
5.4%
지 399
 
4.9%
번 391
 
4.8%
1 364
 
4.5%
동 338
 
4.2%
호 328
 
4.0%
시 306
 
3.8%
리 297
 
3.7%
Other values (121) 3067
37.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4341
53.6%
Decimal Number 1920
23.7%
Space Separator 1702
 
21.0%
Dash Punctuation 77
 
1.0%
Lowercase Letter 42
 
0.5%
Other Punctuation 15
 
0.2%
Close Punctuation 3
 
< 0.1%
Open Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 470
10.8%
천 441
 
10.2%
지 399
 
9.2%
번 391
 
9.0%
동 338
 
7.8%
호 328
 
7.6%
시 306
 
7.0%
리 297
 
6.8%
면 135
 
3.1%
읍 124
 
2.9%
Other values (95) 1112
25.6%
Decimal Number
ValueCountFrequency (%)
1 364
19.0%
4 281
14.6%
2 246
12.8%
3 212
11.0%
8 157
8.2%
6 154
8.0%
5 138
 
7.2%
7 137
 
7.1%
9 127
 
6.6%
0 104
 
5.4%
Lowercase Letter
ValueCountFrequency (%)
w 9
21.4%
t 6
14.3%
l 6
14.3%
m 6
14.3%
o 3
 
7.1%
a 3
 
7.1%
h 3
 
7.1%
c 3
 
7.1%
p 3
 
7.1%
Other Punctuation
ValueCountFrequency (%)
. 6
40.0%
/ 6
40.0%
: 3
20.0%
Space Separator
ValueCountFrequency (%)
1702
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 77
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4341
53.6%
Common 3720
45.9%
Latin 42
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 470
10.8%
천 441
 
10.2%
지 399
 
9.2%
번 391
 
9.0%
동 338
 
7.8%
호 328
 
7.6%
시 306
 
7.0%
리 297
 
6.8%
면 135
 
3.1%
읍 124
 
2.9%
Other values (95) 1112
25.6%
Common
ValueCountFrequency (%)
1702
45.8%
1 364
 
9.8%
4 281
 
7.6%
2 246
 
6.6%
3 212
 
5.7%
8 157
 
4.2%
6 154
 
4.1%
5 138
 
3.7%
7 137
 
3.7%
9 127
 
3.4%
Other values (7) 202
 
5.4%
Latin
ValueCountFrequency (%)
w 9
21.4%
t 6
14.3%
l 6
14.3%
m 6
14.3%
o 3
 
7.1%
a 3
 
7.1%
h 3
 
7.1%
c 3
 
7.1%
p 3
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4341
53.6%
ASCII 3762
46.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1702
45.2%
1 364
 
9.7%
4 281
 
7.5%
2 246
 
6.5%
3 212
 
5.6%
8 157
 
4.2%
6 154
 
4.1%
5 138
 
3.7%
7 137
 
3.6%
9 127
 
3.4%
Other values (16) 244
 
6.5%
Hangul
ValueCountFrequency (%)
사 470
10.8%
천 441
 
10.2%
지 399
 
9.2%
번 391
 
9.0%
동 338
 
7.8%
호 328
 
7.6%
시 306
 
7.0%
리 297
 
6.8%
면 135
 
3.1%
읍 124
 
2.9%
Other values (95) 1112
25.6%

동영상 링크
Text

MISSING 

Distinct10
Distinct (%)100.0%
Missing532
Missing (%)98.2%
Memory size4.4 KiB
2023-12-12T13:30:07.875001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length32
Mean length32.5
Min length32

Characters and Unicode

Total characters325
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)100.0%

Sample

1st row/mtour/Common/Data/movie/620.mp4
2nd row/mtour/Common/Data/movie/631.mp4
3rd row/mtour/Common/Data/movie/632.mp4
4th row/mtour/Common/Data/movie/789.mp4
5th row/mtour/Common/Data/movie/740.mp4
ValueCountFrequency (%)
mtour/common/data/movie/620.mp4 1
10.0%
mtour/common/data/movie/631.mp4 1
10.0%
mtour/common/data/movie/632.mp4 1
10.0%
mtour/common/data/movie/789.mp4 1
10.0%
mtour/common/data/movie/740.mp4 1
10.0%
mtour/common/data/movie/755.mp4 1
10.0%
mtour/common/data/movie/779.mp4 1
10.0%
mtour/common/data/movie/aviation.mp4 1
10.0%
mtour/common/data/movie/840.mp4 1
10.0%
mtour/common/data/movie/895.mp4 1
10.0%
2023-12-12T13:30:08.152888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 50
15.4%
m 50
15.4%
o 41
12.6%
a 22
 
6.8%
t 21
 
6.5%
i 12
 
3.7%
4 12
 
3.7%
n 11
 
3.4%
v 11
 
3.4%
p 10
 
3.1%
Other values (15) 85
26.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 208
64.0%
Other Punctuation 60
 
18.5%
Decimal Number 37
 
11.4%
Uppercase Letter 20
 
6.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 50
24.0%
o 41
19.7%
a 22
10.6%
t 21
10.1%
i 12
 
5.8%
n 11
 
5.3%
v 11
 
5.3%
p 10
 
4.8%
e 10
 
4.8%
r 10
 
4.8%
Decimal Number
ValueCountFrequency (%)
4 12
32.4%
7 5
13.5%
6 3
 
8.1%
0 3
 
8.1%
8 3
 
8.1%
9 3
 
8.1%
5 3
 
8.1%
2 2
 
5.4%
3 2
 
5.4%
1 1
 
2.7%
Other Punctuation
ValueCountFrequency (%)
/ 50
83.3%
. 10
 
16.7%
Uppercase Letter
ValueCountFrequency (%)
D 10
50.0%
C 10
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 228
70.2%
Common 97
29.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 50
21.9%
o 41
18.0%
a 22
9.6%
t 21
9.2%
i 12
 
5.3%
n 11
 
4.8%
v 11
 
4.8%
p 10
 
4.4%
e 10
 
4.4%
D 10
 
4.4%
Other values (3) 30
13.2%
Common
ValueCountFrequency (%)
/ 50
51.5%
4 12
 
12.4%
. 10
 
10.3%
7 5
 
5.2%
6 3
 
3.1%
0 3
 
3.1%
8 3
 
3.1%
9 3
 
3.1%
5 3
 
3.1%
2 2
 
2.1%
Other values (2) 3
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 325
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 50
15.4%
m 50
15.4%
o 41
12.6%
a 22
 
6.8%
t 21
 
6.5%
i 12
 
3.7%
4 12
 
3.7%
n 11
 
3.4%
v 11
 
3.4%
p 10
 
3.1%
Other values (15) 85
26.2%

vr 링크
Text

MISSING 

Distinct16
Distinct (%)100.0%
Missing526
Missing (%)97.0%
Memory size4.4 KiB
2023-12-12T13:30:08.366420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters576
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)100.0%

Sample

1st row/mtour/Common/Data/panorama/619.html
2nd row/mtour/Common/Data/panorama/620.html
3rd row/mtour/Common/Data/panorama/625.html
4th row/mtour/Common/Data/panorama/628.html
5th row/mtour/Common/Data/panorama/634.html
ValueCountFrequency (%)
mtour/common/data/panorama/758.html 1
 
6.2%
mtour/common/data/panorama/625.html 1
 
6.2%
mtour/common/data/panorama/628.html 1
 
6.2%
mtour/common/data/panorama/634.html 1
 
6.2%
mtour/common/data/panorama/637.html 1
 
6.2%
mtour/common/data/panorama/757.html 1
 
6.2%
mtour/common/data/panorama/620.html 1
 
6.2%
mtour/common/data/panorama/776.html 1
 
6.2%
mtour/common/data/panorama/619.html 1
 
6.2%
mtour/common/data/panorama/779.html 1
 
6.2%
Other values (6) 6
37.5%
2023-12-12T13:30:08.740300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 80
13.9%
a 80
13.9%
m 80
13.9%
o 64
11.1%
t 48
8.3%
r 32
 
5.6%
n 32
 
5.6%
l 16
 
2.8%
u 16
 
2.8%
C 16
 
2.8%
Other values (14) 112
19.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 400
69.4%
Other Punctuation 96
 
16.7%
Decimal Number 48
 
8.3%
Uppercase Letter 32
 
5.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 80
20.0%
m 80
20.0%
o 64
16.0%
t 48
12.0%
r 32
 
8.0%
n 32
 
8.0%
l 16
 
4.0%
u 16
 
4.0%
p 16
 
4.0%
h 16
 
4.0%
Decimal Number
ValueCountFrequency (%)
7 9
18.8%
6 8
16.7%
9 7
14.6%
8 7
14.6%
5 5
10.4%
2 4
8.3%
1 4
8.3%
3 2
 
4.2%
4 1
 
2.1%
0 1
 
2.1%
Other Punctuation
ValueCountFrequency (%)
/ 80
83.3%
. 16
 
16.7%
Uppercase Letter
ValueCountFrequency (%)
C 16
50.0%
D 16
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 432
75.0%
Common 144
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 80
55.6%
. 16
 
11.1%
7 9
 
6.2%
6 8
 
5.6%
9 7
 
4.9%
8 7
 
4.9%
5 5
 
3.5%
2 4
 
2.8%
1 4
 
2.8%
3 2
 
1.4%
Other values (2) 2
 
1.4%
Latin
ValueCountFrequency (%)
a 80
18.5%
m 80
18.5%
o 64
14.8%
t 48
11.1%
r 32
 
7.4%
n 32
 
7.4%
l 16
 
3.7%
u 16
 
3.7%
C 16
 
3.7%
D 16
 
3.7%
Other values (2) 32
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 80
13.9%
a 80
13.9%
m 80
13.9%
o 64
11.1%
t 48
8.3%
r 32
 
5.6%
n 32
 
5.6%
l 16
 
2.8%
u 16
 
2.8%
C 16
 
2.8%
Other values (14) 112
19.4%

값1
Text

MISSING 

Distinct411
Distinct (%)93.4%
Missing102
Missing (%)18.8%
Memory size4.4 KiB
2023-12-12T13:30:09.120269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length7.6568182
Min length3

Characters and Unicode

Total characters3369
Distinct characters146
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique388 ?
Unique (%)88.2%

Sample

1st row진삼로 406
2nd row다솔사길 417
3rd row수도골안길 24
4th row굴항길
5th row사남면 유천리 805
ValueCountFrequency (%)
진삼로 37
 
4.2%
목섬길 26
 
3.0%
사천대로 14
 
1.6%
선진공원길 12
 
1.4%
남일로 11
 
1.2%
동금2길 10
 
1.1%
69 10
 
1.1%
사주길 9
 
1.0%
수양로 9
 
1.0%
노산공원길 8
 
0.9%
Other values (408) 734
83.4%
2023-12-12T13:30:09.877999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
440
 
13.1%
길 278
 
8.3%
1 276
 
8.2%
로 161
 
4.8%
2 152
 
4.5%
4 136
 
4.0%
3 134
 
4.0%
7 116
 
3.4%
5 110
 
3.3%
9 99
 
2.9%
Other values (136) 1467
43.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1541
45.7%
Decimal Number 1286
38.2%
Space Separator 440
 
13.1%
Dash Punctuation 94
 
2.8%
Close Punctuation 4
 
0.1%
Open Punctuation 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
길 278
 
18.0%
로 161
 
10.4%
진 55
 
3.6%
사 53
 
3.4%
삼 47
 
3.0%
동 47
 
3.0%
선 36
 
2.3%
공 30
 
1.9%
대 30
 
1.9%
천 28
 
1.8%
Other values (122) 776
50.4%
Decimal Number
ValueCountFrequency (%)
1 276
21.5%
2 152
11.8%
4 136
10.6%
3 134
10.4%
7 116
9.0%
5 110
 
8.6%
9 99
 
7.7%
8 95
 
7.4%
6 90
 
7.0%
0 78
 
6.1%
Space Separator
ValueCountFrequency (%)
440
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 94
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1828
54.3%
Hangul 1541
45.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
길 278
 
18.0%
로 161
 
10.4%
진 55
 
3.6%
사 53
 
3.4%
삼 47
 
3.0%
동 47
 
3.0%
선 36
 
2.3%
공 30
 
1.9%
대 30
 
1.9%
천 28
 
1.8%
Other values (122) 776
50.4%
Common
ValueCountFrequency (%)
440
24.1%
1 276
15.1%
2 152
 
8.3%
4 136
 
7.4%
3 134
 
7.3%
7 116
 
6.3%
5 110
 
6.0%
9 99
 
5.4%
8 95
 
5.2%
- 94
 
5.1%
Other values (4) 176
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1828
54.3%
Hangul 1541
45.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
440
24.1%
1 276
15.1%
2 152
 
8.3%
4 136
 
7.4%
3 134
 
7.3%
7 116
 
6.3%
5 110
 
6.0%
9 99
 
5.4%
8 95
 
5.2%
- 94
 
5.1%
Other values (4) 176
 
9.6%
Hangul
ValueCountFrequency (%)
길 278
 
18.0%
로 161
 
10.4%
진 55
 
3.6%
사 53
 
3.4%
삼 47
 
3.0%
동 47
 
3.0%
선 36
 
2.3%
공 30
 
1.9%
대 30
 
1.9%
천 28
 
1.8%
Other values (122) 776
50.4%

위도
Real number (ℝ)

MISSING 

Distinct502
Distinct (%)98.8%
Missing34
Missing (%)6.3%
Infinite0
Infinite (%)0.0%
Mean35.00293
Minimum34.903455
Maximum35.156729
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2023-12-12T13:30:10.117028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.903455
5-th percentile34.924549
Q134.932105
median34.98646
Q335.080505
95-th percentile35.107094
Maximum35.156729
Range0.25327329
Interquartile range (IQR)0.1484

Descriptive statistics

Standard deviation0.072669033
Coefficient of variation (CV)0.0020760842
Kurtosis-1.5768521
Mean35.00293
Median Absolute Deviation (MAD)0.0597267
Skewness0.26354099
Sum17781.489
Variance0.0052807883
MonotonicityNot monotonic
2023-12-12T13:30:10.334775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.9321312 2
 
0.4%
35.1311659 2
 
0.4%
34.92575544 2
 
0.4%
35.0828152 2
 
0.4%
35.0937117 2
 
0.4%
35.08291666 2
 
0.4%
35.0853962 1
 
0.2%
34.9282702 1
 
0.2%
35.0787321 1
 
0.2%
35.0782088 1
 
0.2%
Other values (492) 492
90.8%
(Missing) 34
 
6.3%
ValueCountFrequency (%)
34.90345531 1
0.2%
34.92043612 1
0.2%
34.9216096 1
0.2%
34.9235074 1
0.2%
34.9235877 1
0.2%
34.92361806 1
0.2%
34.9238013 1
0.2%
34.92398487 1
0.2%
34.9240215 1
0.2%
34.9241097 1
0.2%
ValueCountFrequency (%)
35.1567286 1
0.2%
35.1456269 1
0.2%
35.1415786 1
0.2%
35.141459 1
0.2%
35.1412945 1
0.2%
35.1411386 1
0.2%
35.1411131 1
0.2%
35.1409423 1
0.2%
35.14089502 1
0.2%
35.1407641 1
0.2%

경도
Real number (ℝ)

MISSING 

Distinct503
Distinct (%)99.0%
Missing34
Missing (%)6.3%
Infinite0
Infinite (%)0.0%
Mean128.06273
Minimum127.91072
Maximum128.13316
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2023-12-12T13:30:10.534709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.91072
5-th percentile127.96144
Q1128.05615
median128.07649
Q3128.08564
95-th percentile128.09776
Maximum128.13316
Range0.2224377
Interquartile range (IQR)0.029489825

Descriptive statistics

Standard deviation0.042137231
Coefficient of variation (CV)0.00032903586
Kurtosis2.7511312
Mean128.06273
Median Absolute Deviation (MAD)0.0100614
Skewness-1.8008617
Sum65055.867
Variance0.0017755462
MonotonicityNot monotonic
2023-12-12T13:30:10.740513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.9198928 2
 
0.4%
128.0565336 2
 
0.4%
127.919915 2
 
0.4%
128.0743632 2
 
0.4%
128.0755978 2
 
0.4%
128.0796127 1
 
0.2%
128.0839058 1
 
0.2%
128.0839371 1
 
0.2%
128.0696601 1
 
0.2%
128.0836853 1
 
0.2%
Other values (493) 493
91.0%
(Missing) 34
 
6.3%
ValueCountFrequency (%)
127.9107193 1
0.2%
127.9198928 2
0.4%
127.919915 2
0.4%
127.9233052 1
0.2%
127.9252683 1
0.2%
127.9265295 1
0.2%
127.926769 1
0.2%
127.9273487 1
0.2%
127.9339527 1
0.2%
127.9382077 1
0.2%
ValueCountFrequency (%)
128.133157 1
0.2%
128.1310635 1
0.2%
128.1281526 1
0.2%
128.1272603 1
0.2%
128.1242936 1
0.2%
128.1232971 1
0.2%
128.1200263 1
0.2%
128.1191791 1
0.2%
128.1180359 1
0.2%
128.1174309 1
0.2%

계절
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
<NA>
522 
spring
 
6
summer
 
6
autumm
 
5
winter
 
3

Length

Max length6
Median length4
Mean length4.0738007
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowspring
2nd row<NA>
3rd rowspring
4th row<NA>
5th rowsummer

Common Values

ValueCountFrequency (%)
<NA> 522
96.3%
spring 6
 
1.1%
summer 6
 
1.1%
autumm 5
 
0.9%
winter 3
 
0.6%

Length

2023-12-12T13:30:10.917231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:30:11.037332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 522
96.3%
spring 6
 
1.1%
summer 6
 
1.1%
autumm 5
 
0.9%
winter 3
 
0.6%

Interactions

2023-12-12T13:30:01.837037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:00.710352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:01.127501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:01.958146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:00.836298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:01.633839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:02.066697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:00.988485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:30:01.733557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:30:11.127355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조회수등록일위치시간홈페이지동영상 링크vr 링크위도경도계절
조회수1.0000.7430.9490.1261.0001.0001.0000.2230.3410.000
등록일0.7431.0000.9720.8761.0001.0001.0000.7170.7110.467
위치0.9490.9721.000NaN1.0001.0001.0000.9960.9971.000
시간0.1260.876NaN1.000NaNNaNNaN0.2020.000NaN
홈페이지1.0001.0001.000NaN1.0001.0000.0001.0001.000NaN
동영상 링크1.0001.0001.000NaN1.0001.0001.0001.0001.000NaN
vr 링크1.0001.0001.000NaN0.0001.0001.0001.0001.0001.000
위도0.2230.7170.9960.2021.0001.0001.0001.0000.8230.000
경도0.3410.7110.9970.0001.0001.0001.0000.8231.0000.534
계절0.0000.4671.000NaNNaNNaN1.0000.0000.5341.000
2023-12-12T13:30:11.292971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록일계절
등록일1.0000.172
계절0.1721.000
2023-12-12T13:30:11.708875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조회수위도경도등록일계절
조회수1.000-0.036-0.1840.3810.000
위도-0.0361.0000.1170.3560.000
경도-0.1840.1171.0000.3500.335
등록일0.3810.3560.3501.0000.172
계절0.0000.0000.3350.1721.000

Missing values

2023-12-12T13:30:02.252864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:30:02.480673image/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.
2023-12-12T13:30:02.734994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

제목조회수등록일주소위치시간홈페이지지도 주소동영상 링크vr 링크값1위도경도계절
0卧龙山12532013-04-30노룡동 1번지사천시 사남면, 용현면, 벌용동, 남양동<NA><NA>노룡동 1번지<NA>/mtour/Common/Data/panorama/619.html진삼로 40634.985498128.113926spring
1凤鸣山8372013-04-30곤명면 용산리 86일원사천공항에서 22km<NA><NA>사천시 곤명면 용산리 86/mtour/Common/Data/movie/620.mp4/mtour/Common/Data/panorama/620.html다솔사길 41735.082917127.919893<NA>
2角山8452013-04-30사천시 동림동 190일원사천공항에서 18Km<NA><NA>사천시 동림동 190<NA><NA>수도골안길 2434.943346128.058944spring
3闲丽水道7782013-04-30늑도동 477 일원(초양휴게소)삼천포항 일원<NA><NA>사천시 늑도동 477<NA><NA><NA>34.925587128.046752<NA>
4南逸台海水浴场11042013-04-30향촌동 710 일원삼천포항에서 동쪽으로 3.5㎞<NA><NA>향촌동 710 일원<NA><NA><NA>34.926476128.096669summer
5象石11132013-04-30향촌동 710 일원내삼천포항에서 동쪽으로 3.5Km<NA><NA>향촌동 710 일원 내<NA><NA><NA>34.924622128.097838summer
6大芳镇掘港12172013-04-30대방동 250삼천포 시외버스터미널에서 3Km<NA><NA>대방동 250<NA>/mtour/Common/Data/panorama/625.html굴항길34.929053128.056777spring
7竹防簾8802013-04-30삼천포항 일대삼천포항(실안해안)<NA><NA>사천시 삼천포 카페리항<NA><NA><NA>34.923588128.076656winter
8三千浦港11832013-04-30서동 삼천포항 일대사천IC에서 22km<NA><NA>사천시 선진리성<NA><NA>사남면 유천리 80535.071467128.062905<NA>
9实安海岸路14092013-04-30실안동 1254일원실안해안도로 일원<NA><NA>실안동 1254<NA>/mtour/Common/Data/panorama/628.html노을길34.938405128.043115autumm
제목조회수등록일주소위치시간홈페이지지도 주소동영상 링크vr 링크값1위도경도계절
532장터돼지국밥102013-06-17동동 184번지 34호<NA>06:30 ~ 21:00<NA>사천시 동동 184번지 34호<NA><NA>수남3길 3434.928048128.070616<NA>
533정가네생고기122013-06-17동금동 55번지 22호<NA>11:00 ~ 22:30<NA>사천시 동금동 55번지 22호<NA><NA>새시장길 4934.931347128.080562<NA>
534주공칼국수282013-06-17동금동 62번지 14호<NA><NA><NA>사천시 동금동 62번지 14호<NA><NA>동금5길 3334.934123128.082374<NA>
535털보해물전골152013-06-17동동 173번지 27호<NA>12:00 ~ 20:00<NA>사천시 동동 173번지 27호<NA><NA>수남3길 3134.928124128.070879<NA>
536풍년식당102013-06-17동금동 88번지 4호 경남상가 라동동 13<NA><NA><NA>사천시 동금동 88번지 4호<NA><NA>동금2길 1534.932122128.078632<NA>
537할매식당362013-06-17동금동 40번지 2호<NA><NA><NA>사천시 동금동 40번지 2호<NA><NA>남일로 9434.931771128.086544<NA>
538행운선지국밥172013-06-17동금동 88번지 4호 경남상가 다동 7호<NA><NA><NA>사천시 동금동 88번지 4호<NA><NA>동금2길 1534.932131128.078635<NA>
539향촌복집222013-06-17동금동 40번지 1호<NA><NA><NA>사천시 동금동 40번지 1호<NA><NA>남일로 94-134.931678128.08672<NA>
540Hongcheonttukbaegi2342013-06-17동금동 336번지 5호<NA><NA><NA>동금동 336번지 5호<NA><NA>동금로 3034.930635128.078279<NA>
541황소막창782013-06-17동금동 65번지 5호<NA>17:00~05:00<NA>사천시 동금동 65번지 5호<NA><NA>삼상로 42-134.934937128.08085<NA>