Overview

Dataset statistics

Number of variables11
Number of observations552
Missing cells1086
Missing cells (%)17.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory49.2 KiB
Average record size in memory91.2 B

Variable types

Text6
Numeric3
Categorical2

Dataset

Description경상남도 사천시 문화관광홈페이지 전시내용 테이블 자료(제목,조회수,등록일, 주소,위도, 경도 등)에 대한 자료(일본어) 입니다.
Author경상남도 사천시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15084119

Alerts

계절 is highly imbalanced (87.3%)Imbalance
위치 has 457 (82.8%) missing valuesMissing
홈페이지 has 502 (90.9%) missing valuesMissing
지도 주소 has 10 (1.8%) missing valuesMissing
값1 has 83 (15.0%) missing valuesMissing
위도 has 17 (3.1%) missing valuesMissing
경도 has 17 (3.1%) missing valuesMissing

Reproduction

Analysis started2023-12-10 23:40:09.436693
Analysis finished2023-12-10 23:40:11.666343
Duration2.23 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

제목
Text

Distinct542
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
2023-12-11T08:40:11.831209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length5.4619565
Min length1

Characters and Unicode

Total characters3015
Distinct characters701
Distinct categories10 ?
Distinct scripts6 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique534 ?
Unique (%)96.7%

Sample

1st row臥山
2nd row鳳鳴山
3rd row角山
4th row閑麗水道
5th row南逸台海水浴場
ValueCountFrequency (%)
11
 
1.8%
사천 5
 
0.8%
刺身屋 4
 
0.7%
泗川船津里城 2
 
0.3%
大芳掘港 2
 
0.3%
海刺身屋 2
 
0.3%
エムモテル 2
 
0.3%
多率寺 2
 
0.3%
vモテル 2
 
0.3%
민박 2
 
0.3%
Other values (565) 568
94.4%
2023-12-11T08:40:12.190800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
ル 107
 
3.5%
テ 93
 
3.1%
モ 89
 
3.0%
身 63
 
2.1%
刺 62
 
2.1%
屋 53
 
1.8%
52
 
1.7%
ン 42
 
1.4%
場 38
 
1.3%
식 34
 
1.1%
Other values (691) 2382
79.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2894
96.0%
Space Separator 52
 
1.7%
Decimal Number 18
 
0.6%
Uppercase Letter 13
 
0.4%
Dash Punctuation 11
 
0.4%
Close Punctuation 8
 
0.3%
Open Punctuation 8
 
0.3%
Lowercase Letter 5
 
0.2%
Other Punctuation 5
 
0.2%
Letter Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ル 107
 
3.7%
テ 93
 
3.2%
モ 89
 
3.1%
身 63
 
2.2%
刺 62
 
2.1%
屋 53
 
1.8%
ン 42
 
1.5%
場 38
 
1.3%
식 34
 
1.2%
館 34
 
1.2%
Other values (665) 2279
78.7%
Uppercase Letter
ValueCountFrequency (%)
Q 2
15.4%
F 2
15.4%
V 2
15.4%
C 1
7.7%
X 1
7.7%
O 1
7.7%
B 1
7.7%
N 1
7.7%
W 1
7.7%
D 1
7.7%
Decimal Number
ValueCountFrequency (%)
0 4
22.2%
7 3
16.7%
3 3
16.7%
8 2
11.1%
1 2
11.1%
4 2
11.1%
2 1
 
5.6%
9 1
 
5.6%
Other Punctuation
ValueCountFrequency (%)
& 3
60.0%
. 2
40.0%
Space Separator
ValueCountFrequency (%)
52
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Lowercase Letter
ValueCountFrequency (%)
m 5
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1170
38.8%
Han 991
32.9%
Katakana 686
22.8%
Common 102
 
3.4%
Hiragana 47
 
1.6%
Latin 19
 
0.6%

Most frequent character per script

Han
ValueCountFrequency (%)
身 63
 
6.4%
刺 62
 
6.3%
屋 53
 
5.3%
場 38
 
3.8%
館 34
 
3.4%
旅 32
 
3.2%
川 21
 
2.1%
大 19
 
1.9%
村 19
 
1.9%
三 18
 
1.8%
Other values (284) 632
63.8%
Hangul
ValueCountFrequency (%)
식 34
 
2.9%
당 29
 
2.5%
사 23
 
2.0%
장 22
 
1.9%
가 22
 
1.9%
산 22
 
1.9%
집 21
 
1.8%
천 20
 
1.7%
정 17
 
1.5%
구 16
 
1.4%
Other values (276) 944
80.7%
Katakana
ValueCountFrequency (%)
ル 107
15.6%
テ 93
 
13.6%
モ 89
 
13.0%
ン 42
 
6.1%
ス 20
 
2.9%
イ 19
 
2.8%
リ 18
 
2.6%
ク 15
 
2.2%
ラ 12
 
1.7%
チ 12
 
1.7%
Other values (61) 259
37.8%
Hiragana
ValueCountFrequency (%)
の 6
 
12.8%
り 4
 
8.5%
ら 4
 
8.5%
ぐ 3
 
6.4%
い 3
 
6.4%
と 3
 
6.4%
き 3
 
6.4%
た 2
 
4.3%
わ 2
 
4.3%
ん 2
 
4.3%
Other values (14) 15
31.9%
Common
ValueCountFrequency (%)
52
51.0%
- 11
 
10.8%
) 8
 
7.8%
( 8
 
7.8%
0 4
 
3.9%
& 3
 
2.9%
7 3
 
2.9%
3 3
 
2.9%
8 2
 
2.0%
1 2
 
2.0%
Other values (4) 6
 
5.9%
Latin
ValueCountFrequency (%)
m 5
26.3%
Q 2
 
10.5%
F 2
 
10.5%
V 2
 
10.5%
C 1
 
5.3%
X 1
 
5.3%
O 1
 
5.3%
B 1
 
5.3%
N 1
 
5.3%
W 1
 
5.3%
Other values (2) 2
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1170
38.8%
CJK 990
32.8%
Katakana 686
22.8%
ASCII 120
 
4.0%
Hiragana 47
 
1.6%
CJK Compat Ideographs 1
 
< 0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

Katakana
ValueCountFrequency (%)
ル 107
15.6%
テ 93
 
13.6%
モ 89
 
13.0%
ン 42
 
6.1%
ス 20
 
2.9%
イ 19
 
2.8%
リ 18
 
2.6%
ク 15
 
2.2%
ラ 12
 
1.7%
チ 12
 
1.7%
Other values (61) 259
37.8%
CJK
ValueCountFrequency (%)
身 63
 
6.4%
刺 62
 
6.3%
屋 53
 
5.4%
場 38
 
3.8%
館 34
 
3.4%
旅 32
 
3.2%
川 21
 
2.1%
大 19
 
1.9%
村 19
 
1.9%
三 18
 
1.8%
Other values (283) 631
63.7%
ASCII
ValueCountFrequency (%)
52
43.3%
- 11
 
9.2%
) 8
 
6.7%
( 8
 
6.7%
m 5
 
4.2%
0 4
 
3.3%
& 3
 
2.5%
7 3
 
2.5%
3 3
 
2.5%
Q 2
 
1.7%
Other values (15) 21
17.5%
Hangul
ValueCountFrequency (%)
식 34
 
2.9%
당 29
 
2.5%
사 23
 
2.0%
장 22
 
1.9%
가 22
 
1.9%
산 22
 
1.9%
집 21
 
1.8%
천 20
 
1.7%
정 17
 
1.5%
구 16
 
1.4%
Other values (276) 944
80.7%
Hiragana
ValueCountFrequency (%)
の 6
 
12.8%
り 4
 
8.5%
ら 4
 
8.5%
ぐ 3
 
6.4%
い 3
 
6.4%
と 3
 
6.4%
き 3
 
6.4%
た 2
 
4.3%
わ 2
 
4.3%
ん 2
 
4.3%
Other values (14) 15
31.9%
CJK Compat Ideographs
ValueCountFrequency (%)
龍 1
100.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

조회수
Real number (ℝ)

Distinct201
Distinct (%)36.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean151.56703
Minimum1
Maximum1422
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2023-12-11T08:40:12.343207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q19
median19
Q3197
95-th percentile797.6
Maximum1422
Range1421
Interquartile range (IQR)188

Descriptive statistics

Standard deviation260.41069
Coefficient of variation (CV)1.7181223
Kurtosis6.7000957
Mean151.56703
Median Absolute Deviation (MAD)16
Skewness2.5639092
Sum83665
Variance67813.727
MonotonicityNot monotonic
2023-12-11T08:40:12.460508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 27
 
4.9%
2 23
 
4.2%
3 22
 
4.0%
12 21
 
3.8%
4 18
 
3.3%
9 18
 
3.3%
11 18
 
3.3%
8 15
 
2.7%
5 15
 
2.7%
7 14
 
2.5%
Other values (191) 361
65.4%
ValueCountFrequency (%)
1 14
2.5%
2 23
4.2%
3 22
4.0%
4 18
3.3%
5 15
2.7%
6 13
2.4%
7 14
2.5%
8 15
2.7%
9 18
3.3%
10 27
4.9%
ValueCountFrequency (%)
1422 1
0.2%
1393 1
0.2%
1350 1
0.2%
1337 1
0.2%
1252 1
0.2%
1191 1
0.2%
1162 1
0.2%
1150 1
0.2%
1105 1
0.2%
1095 1
0.2%

등록일
Categorical

Distinct22
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
2013-06-10
162 
2013-05-02
76 
2013-06-05
66 
2013-05-06
43 
2013-05-08
43 
Other values (17)
162 

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
29.3%
2013-05-02 76
13.8%
2013-06-05 66
12.0%
2013-05-06 43
 
7.8%
2013-05-08 43
 
7.8%
2013-06-17 42
 
7.6%
2013-06-04 22
 
4.0%
2013-05-01 22
 
4.0%
2013-04-30 18
 
3.3%
2013-05-10 14
 
2.5%
Other values (12) 44
 
8.0%

Length

2023-12-11T08:40:12.585427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2013-06-10 162
29.3%
2013-05-02 76
13.8%
2013-06-05 66
12.0%
2013-05-06 43
 
7.8%
2013-05-08 43
 
7.8%
2013-06-17 42
 
7.6%
2013-06-04 22
 
4.0%
2013-05-01 22
 
4.0%
2013-04-30 18
 
3.3%
2013-05-10 14
 
2.5%
Other values (12) 44
 
8.0%

주소
Text

Distinct533
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
2023-12-11T08:40:12.879166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length22
Mean length14.019928
Min length6

Characters and Unicode

Total characters7739
Distinct characters140
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

Unique521 ?
Unique (%)94.4%

Sample

1st row노룡동 1번지
2nd row곤명면 용산리 86일원
3rd row사천시 동림동 190일원
4th row늑도동 477 일원(초양휴게소)
5th row향촌동 710 일원
ValueCountFrequency (%)
사천읍 124
 
6.9%
동금동 49
 
2.7%
수석리 49
 
2.7%
서금동 45
 
2.5%
벌리동 43
 
2.4%
1호 41
 
2.3%
용현면 37
 
2.1%
곤명면 35
 
1.9%
4호 34
 
1.9%
3호 32
 
1.8%
Other values (529) 1313
72.9%
2023-12-11T08:40:13.534042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1584
20.5%
지 420
 
5.4%
1 408
 
5.3%
번 407
 
5.3%
동 383
 
4.9%
호 342
 
4.4%
리 315
 
4.1%
4 298
 
3.9%
2 270
 
3.5%
3 238
 
3.1%
Other values (130) 3074
39.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3897
50.4%
Decimal Number 2107
27.2%
Space Separator 1584
20.5%
Dash Punctuation 101
 
1.3%
Open Punctuation 16
 
0.2%
Close Punctuation 16
 
0.2%
Other Punctuation 16
 
0.2%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 420
 
10.8%
번 407
 
10.4%
동 383
 
9.8%
호 342
 
8.8%
리 315
 
8.1%
사 195
 
5.0%
천 170
 
4.4%
면 154
 
4.0%
읍 126
 
3.2%
금 103
 
2.6%
Other values (113) 1282
32.9%
Decimal Number
ValueCountFrequency (%)
1 408
19.4%
4 298
14.1%
2 270
12.8%
3 238
11.3%
8 176
8.4%
6 164
7.8%
7 155
 
7.4%
5 153
 
7.3%
9 144
 
6.8%
0 101
 
4.8%
Other Punctuation
ValueCountFrequency (%)
, 15
93.8%
/ 1
 
6.2%
Space Separator
ValueCountFrequency (%)
1584
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 101
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3897
50.4%
Common 3842
49.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 420
 
10.8%
번 407
 
10.4%
동 383
 
9.8%
호 342
 
8.8%
리 315
 
8.1%
사 195
 
5.0%
천 170
 
4.4%
면 154
 
4.0%
읍 126
 
3.2%
금 103
 
2.6%
Other values (113) 1282
32.9%
Common
ValueCountFrequency (%)
1584
41.2%
1 408
 
10.6%
4 298
 
7.8%
2 270
 
7.0%
3 238
 
6.2%
8 176
 
4.6%
6 164
 
4.3%
7 155
 
4.0%
5 153
 
4.0%
9 144
 
3.7%
Other values (7) 252
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3897
50.4%
ASCII 3842
49.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1584
41.2%
1 408
 
10.6%
4 298
 
7.8%
2 270
 
7.0%
3 238
 
6.2%
8 176
 
4.6%
6 164
 
4.3%
7 155
 
4.0%
5 153
 
4.0%
9 144
 
3.7%
Other values (7) 252
 
6.6%
Hangul
ValueCountFrequency (%)
지 420
 
10.8%
번 407
 
10.4%
동 383
 
9.8%
호 342
 
8.8%
리 315
 
8.1%
사 195
 
5.0%
천 170
 
4.4%
면 154
 
4.0%
읍 126
 
3.2%
금 103
 
2.6%
Other values (113) 1282
32.9%

위치
Text

MISSING 

Distinct88
Distinct (%)92.6%
Missing457
Missing (%)82.8%
Memory size4.4 KiB
2023-12-11T08:40:13.753635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length20
Mean length14.042105
Min length4

Characters and Unicode

Total characters1334
Distinct characters130
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique82 ?
Unique (%)86.3%

Sample

1st row사천시 사남면, 용현면, 벌용동, 남양동
2nd row사천공항에서 22km
3rd row사천공항에서 18Km
4th row삼천포항 일원
5th row삼천포항에서 동쪽으로 3.5㎞
ValueCountFrequency (%)
사천ic에서 33
 
13.2%
삼천포항 14
 
5.6%
곤양ic에서 11
 
4.4%
방향으로 10
 
4.0%
사천시 7
 
2.8%
사남면 7
 
2.8%
일원 7
 
2.8%
22km 6
 
2.4%
사천공항에서 4
 
1.6%
23km 4
 
1.6%
Other values (107) 147
58.8%
2023-12-11T08:40:14.101899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
206
 
15.4%
천 77
 
5.8%
서 67
 
5.0%
사 62
 
4.6%
에 62
 
4.6%
m 59
 
4.4%
C 48
 
3.6%
I 48
 
3.6%
k 42
 
3.1%
2 30
 
2.2%
Other values (120) 633
47.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 762
57.1%
Space Separator 206
 
15.4%
Uppercase Letter 112
 
8.4%
Decimal Number 111
 
8.3%
Lowercase Letter 101
 
7.6%
Other Punctuation 31
 
2.3%
Open Punctuation 4
 
0.3%
Close Punctuation 4
 
0.3%
Other Symbol 2
 
0.1%
Math Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 77
 
10.1%
서 67
 
8.8%
사 62
 
8.1%
에 62
 
8.1%
포 26
 
3.4%
항 24
 
3.1%
동 23
 
3.0%
삼 23
 
3.0%
양 22
 
2.9%
곤 21
 
2.8%
Other values (98) 355
46.6%
Decimal Number
ValueCountFrequency (%)
2 30
27.0%
1 19
17.1%
5 17
15.3%
0 12
 
10.8%
3 11
 
9.9%
6 9
 
8.1%
4 7
 
6.3%
7 3
 
2.7%
9 2
 
1.8%
8 1
 
0.9%
Uppercase Letter
ValueCountFrequency (%)
C 48
42.9%
I 48
42.9%
K 16
 
14.3%
Lowercase Letter
ValueCountFrequency (%)
m 59
58.4%
k 42
41.6%
Other Punctuation
ValueCountFrequency (%)
, 19
61.3%
. 12
38.7%
Space Separator
ValueCountFrequency (%)
206
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Other Symbol
ValueCountFrequency (%)
㎞ 2
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 762
57.1%
Common 359
26.9%
Latin 213
 
16.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 77
 
10.1%
서 67
 
8.8%
사 62
 
8.1%
에 62
 
8.1%
포 26
 
3.4%
항 24
 
3.1%
동 23
 
3.0%
삼 23
 
3.0%
양 22
 
2.9%
곤 21
 
2.8%
Other values (98) 355
46.6%
Common
ValueCountFrequency (%)
206
57.4%
2 30
 
8.4%
1 19
 
5.3%
, 19
 
5.3%
5 17
 
4.7%
. 12
 
3.3%
0 12
 
3.3%
3 11
 
3.1%
6 9
 
2.5%
4 7
 
1.9%
Other values (7) 17
 
4.7%
Latin
ValueCountFrequency (%)
m 59
27.7%
C 48
22.5%
I 48
22.5%
k 42
19.7%
K 16
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 762
57.1%
ASCII 570
42.7%
CJK Compat 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
206
36.1%
m 59
 
10.4%
C 48
 
8.4%
I 48
 
8.4%
k 42
 
7.4%
2 30
 
5.3%
1 19
 
3.3%
, 19
 
3.3%
5 17
 
3.0%
K 16
 
2.8%
Other values (11) 66
 
11.6%
Hangul
ValueCountFrequency (%)
천 77
 
10.1%
서 67
 
8.8%
사 62
 
8.1%
에 62
 
8.1%
포 26
 
3.4%
항 24
 
3.1%
동 23
 
3.0%
삼 23
 
3.0%
양 22
 
2.9%
곤 21
 
2.8%
Other values (98) 355
46.6%
CJK Compat
ValueCountFrequency (%)
㎞ 2
100.0%

홈페이지
Text

MISSING 

Distinct33
Distinct (%)66.0%
Missing502
Missing (%)90.9%
Memory size4.4 KiB
2023-12-11T08:40:14.282890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length35
Mean length24.84
Min length12

Characters and Unicode

Total characters1242
Distinct characters48
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

Unique32 ?
Unique (%)64.0%

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 19
36.5%
http://www.namiltte.com 2
 
3.8%
http://chistory.ciclife.co.kr 1
 
1.9%
http://www.rmorningsun.com 1
 
1.9%
http://www.3004marina.co.kr 1
 
1.9%
http://blog.naver.com/a0102433 1
 
1.9%
http://bitogaza.com 1
 
1.9%
http://www.seapensun.net 1
 
1.9%
http://www.dajayeon.com 1
 
1.9%
http://damaek.seantour.org 1
 
1.9%
Other values (23) 23
44.2%
2023-12-11T08:40:14.571031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 117
 
9.4%
w 111
 
8.9%
t 108
 
8.7%
. 102
 
8.2%
0 66
 
5.3%
o 66
 
5.3%
m 64
 
5.2%
h 56
 
4.5%
a 56
 
4.5%
p 54
 
4.3%
Other values (38) 442
35.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 820
66.0%
Other Punctuation 267
 
21.5%
Decimal Number 115
 
9.3%
Space Separator 31
 
2.5%
Other Letter 9
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 111
13.5%
t 108
13.2%
o 66
 
8.0%
m 64
 
7.8%
h 56
 
6.8%
a 56
 
6.8%
p 54
 
6.6%
c 47
 
5.7%
l 47
 
5.7%
n 33
 
4.0%
Other values (15) 178
21.7%
Decimal Number
ValueCountFrequency (%)
0 66
57.4%
4 23
 
20.0%
2 6
 
5.2%
6 5
 
4.3%
3 4
 
3.5%
5 4
 
3.5%
1 3
 
2.6%
9 2
 
1.7%
8 1
 
0.9%
7 1
 
0.9%
Other Letter
ValueCountFrequency (%)
밸 1
11.1%
호 1
11.1%
엔 1
11.1%
씨 1
11.1%
지 1
11.1%
번 1
11.1%
동 1
11.1%
서 1
11.1%
리 1
11.1%
Other Punctuation
ValueCountFrequency (%)
/ 117
43.8%
. 102
38.2%
: 48
18.0%
Space Separator
ValueCountFrequency (%)
31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 820
66.0%
Common 413
33.3%
Hangul 9
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 111
13.5%
t 108
13.2%
o 66
 
8.0%
m 64
 
7.8%
h 56
 
6.8%
a 56
 
6.8%
p 54
 
6.6%
c 47
 
5.7%
l 47
 
5.7%
n 33
 
4.0%
Other values (15) 178
21.7%
Common
ValueCountFrequency (%)
/ 117
28.3%
. 102
24.7%
0 66
16.0%
: 48
11.6%
31
 
7.5%
4 23
 
5.6%
2 6
 
1.5%
6 5
 
1.2%
3 4
 
1.0%
5 4
 
1.0%
Other values (4) 7
 
1.7%
Hangul
ValueCountFrequency (%)
밸 1
11.1%
호 1
11.1%
엔 1
11.1%
씨 1
11.1%
지 1
11.1%
번 1
11.1%
동 1
11.1%
서 1
11.1%
리 1
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1233
99.3%
Hangul 9
 
0.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 117
 
9.5%
w 111
 
9.0%
t 108
 
8.8%
. 102
 
8.3%
0 66
 
5.4%
o 66
 
5.4%
m 64
 
5.2%
h 56
 
4.5%
a 56
 
4.5%
p 54
 
4.4%
Other values (29) 433
35.1%
Hangul
ValueCountFrequency (%)
밸 1
11.1%
호 1
11.1%
엔 1
11.1%
씨 1
11.1%
지 1
11.1%
번 1
11.1%
동 1
11.1%
서 1
11.1%
리 1
11.1%

지도 주소
Text

MISSING 

Distinct519
Distinct (%)95.8%
Missing10
Missing (%)1.8%
Memory size4.4 KiB
2023-12-11T08:40:14.795705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length19
Mean length15.337638
Min length3

Characters and Unicode

Total characters8313
Distinct characters117
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

Unique504 ?
Unique (%)93.0%

Sample

1st row노룡동 1번지
2nd row사천시 곤명면 용산리 86
3rd row사천시 동림동 190
4th row사천시 늑도동 477
5th row향촌동 710
ValueCountFrequency (%)
사천시 305
 
15.4%
사천읍 122
 
6.2%
동금동 50
 
2.5%
수석리 48
 
2.4%
서금동 45
 
2.3%
벌리동 43
 
2.2%
1호 41
 
2.1%
용현면 37
 
1.9%
곤명면 34
 
1.7%
4호 33
 
1.7%
Other values (489) 1225
61.8%
2023-12-11T08:40:15.149021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1733
20.8%
사 473
 
5.7%
천 442
 
5.3%
지 403
 
4.8%
번 395
 
4.8%
1 380
 
4.6%
동 372
 
4.5%
호 327
 
3.9%
리 307
 
3.7%
시 306
 
3.7%
Other values (107) 3175
38.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4449
53.5%
Decimal Number 2027
24.4%
Space Separator 1733
 
20.8%
Dash Punctuation 102
 
1.2%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 473
10.6%
천 442
 
9.9%
지 403
 
9.1%
번 395
 
8.9%
동 372
 
8.4%
호 327
 
7.3%
리 307
 
6.9%
시 306
 
6.9%
면 145
 
3.3%
읍 124
 
2.8%
Other values (93) 1155
26.0%
Decimal Number
ValueCountFrequency (%)
1 380
18.7%
4 291
14.4%
2 266
13.1%
3 228
11.2%
8 173
8.5%
6 156
7.7%
5 149
 
7.4%
7 148
 
7.3%
9 139
 
6.9%
0 97
 
4.8%
Space Separator
ValueCountFrequency (%)
1733
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 102
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4449
53.5%
Common 3864
46.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 473
10.6%
천 442
 
9.9%
지 403
 
9.1%
번 395
 
8.9%
동 372
 
8.4%
호 327
 
7.3%
리 307
 
6.9%
시 306
 
6.9%
면 145
 
3.3%
읍 124
 
2.8%
Other values (93) 1155
26.0%
Common
ValueCountFrequency (%)
1733
44.8%
1 380
 
9.8%
4 291
 
7.5%
2 266
 
6.9%
3 228
 
5.9%
8 173
 
4.5%
6 156
 
4.0%
5 149
 
3.9%
7 148
 
3.8%
9 139
 
3.6%
Other values (4) 201
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4449
53.5%
ASCII 3864
46.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1733
44.8%
1 380
 
9.8%
4 291
 
7.5%
2 266
 
6.9%
3 228
 
5.9%
8 173
 
4.5%
6 156
 
4.0%
5 149
 
3.9%
7 148
 
3.8%
9 139
 
3.6%
Other values (4) 201
 
5.2%
Hangul
ValueCountFrequency (%)
사 473
10.6%
천 442
 
9.9%
지 403
 
9.1%
번 395
 
8.9%
동 372
 
8.4%
호 327
 
7.3%
리 307
 
6.9%
시 306
 
6.9%
면 145
 
3.3%
읍 124
 
2.8%
Other values (93) 1155
26.0%

값1
Text

MISSING 

Distinct438
Distinct (%)93.4%
Missing83
Missing (%)15.0%
Memory size4.4 KiB
2023-12-11T08:40:15.447429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length7.7249467
Min length3

Characters and Unicode

Total characters3623
Distinct characters155
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

Unique413 ?
Unique (%)88.1%

Sample

1st row진삼로 406
2nd row다솔사길 417
3rd row수도골안길 24
4th row삼천포대교로 112
5th row남일대길 55
ValueCountFrequency (%)
진삼로 37
 
4.0%
목섬길 26
 
2.8%
사천대로 14
 
1.5%
선진공원길 12
 
1.3%
남일로 11
 
1.2%
동금2길 10
 
1.1%
69 10
 
1.1%
어시장길 9
 
1.0%
수양로 9
 
1.0%
해안관광로 9
 
1.0%
Other values (440) 789
84.3%
2023-12-11T08:40:15.870820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
468
 
12.9%
길 302
 
8.3%
1 295
 
8.1%
2 170
 
4.7%
로 169
 
4.7%
3 150
 
4.1%
4 145
 
4.0%
7 119
 
3.3%
5 118
 
3.3%
- 110
 
3.0%
Other values (145) 1577
43.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1654
45.7%
Decimal Number 1383
38.2%
Space Separator 468
 
12.9%
Dash Punctuation 110
 
3.0%
Close Punctuation 4
 
0.1%
Open Punctuation 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
길 302
 
18.3%
로 169
 
10.2%
진 55
 
3.3%
사 53
 
3.2%
삼 51
 
3.1%
동 47
 
2.8%
선 36
 
2.2%
대 36
 
2.2%
천 33
 
2.0%
공 30
 
1.8%
Other values (131) 842
50.9%
Decimal Number
ValueCountFrequency (%)
1 295
21.3%
2 170
12.3%
3 150
10.8%
4 145
10.5%
7 119
8.6%
5 118
 
8.5%
9 103
 
7.4%
8 102
 
7.4%
6 96
 
6.9%
0 85
 
6.1%
Space Separator
ValueCountFrequency (%)
468
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 110
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1969
54.3%
Hangul 1654
45.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
길 302
 
18.3%
로 169
 
10.2%
진 55
 
3.3%
사 53
 
3.2%
삼 51
 
3.1%
동 47
 
2.8%
선 36
 
2.2%
대 36
 
2.2%
천 33
 
2.0%
공 30
 
1.8%
Other values (131) 842
50.9%
Common
ValueCountFrequency (%)
468
23.8%
1 295
15.0%
2 170
 
8.6%
3 150
 
7.6%
4 145
 
7.4%
7 119
 
6.0%
5 118
 
6.0%
- 110
 
5.6%
9 103
 
5.2%
8 102
 
5.2%
Other values (4) 189
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1969
54.3%
Hangul 1654
45.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
468
23.8%
1 295
15.0%
2 170
 
8.6%
3 150
 
7.6%
4 145
 
7.4%
7 119
 
6.0%
5 118
 
6.0%
- 110
 
5.6%
9 103
 
5.2%
8 102
 
5.2%
Other values (4) 189
9.6%
Hangul
ValueCountFrequency (%)
길 302
 
18.3%
로 169
 
10.2%
진 55
 
3.3%
사 53
 
3.2%
삼 51
 
3.1%
동 47
 
2.8%
선 36
 
2.2%
대 36
 
2.2%
천 33
 
2.0%
공 30
 
1.8%
Other values (131) 842
50.9%

위도
Real number (ℝ)

MISSING 

Distinct529
Distinct (%)98.9%
Missing17
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean35.004347
Minimum34.900726
Maximum35.716281
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2023-12-11T08:40:15.987108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.900726
5-th percentile34.924566
Q134.932124
median34.986471
Q335.080464
95-th percentile35.107129
Maximum35.716281
Range0.81555456
Interquartile range (IQR)0.14833965

Descriptive statistics

Standard deviation0.078708728
Coefficient of variation (CV)0.0022485415
Kurtosis10.735102
Mean35.004347
Median Absolute Deviation (MAD)0.0595453
Skewness1.5686883
Sum18727.326
Variance0.0061950639
MonotonicityNot monotonic
2023-12-11T08:40:16.103271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.08291666 2
 
0.4%
35.0828152 2
 
0.4%
35.0937117 2
 
0.4%
34.92575544 2
 
0.4%
35.1311659 2
 
0.4%
34.9321312 2
 
0.4%
35.0814696 1
 
0.2%
34.9264175 1
 
0.2%
34.9259107 1
 
0.2%
34.9321105 1
 
0.2%
Other values (519) 519
94.0%
(Missing) 17
 
3.1%
ValueCountFrequency (%)
34.9007263 1
0.2%
34.90345531 1
0.2%
34.92043612 1
0.2%
34.9216096 1
0.2%
34.9227084 1
0.2%
34.9235074 1
0.2%
34.92361806 1
0.2%
34.9238013 1
0.2%
34.92398487 1
0.2%
34.9240215 1
0.2%
ValueCountFrequency (%)
35.71628086 1
0.2%
35.2033947 1
0.2%
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%
35.14089502 1
0.2%

경도
Real number (ℝ)

MISSING 

Distinct530
Distinct (%)99.1%
Missing17
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean128.0619
Minimum126.98104
Maximum128.90066
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.0 KiB
2023-12-11T08:40:16.229364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.98104
5-th percentile127.95957
Q1128.05339
median128.07598
Q3128.0857
95-th percentile128.09914
Maximum128.90066
Range1.9196177
Interquartile range (IQR)0.0323041

Descriptive statistics

Standard deviation0.073062855
Coefficient of variation (CV)0.00057052763
Kurtosis121.22372
Mean128.0619
Median Absolute Deviation (MAD)0.0109708
Skewness-3.5796218
Sum68513.118
Variance0.0053381807
MonotonicityNot monotonic
2023-12-11T08:40:16.346147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.919915 2
 
0.4%
128.0755978 2
 
0.4%
128.0743632 2
 
0.4%
128.0565336 2
 
0.4%
127.9198928 2
 
0.4%
128.0857726 1
 
0.2%
128.0751099 1
 
0.2%
128.0836853 1
 
0.2%
128.0845443 1
 
0.2%
128.0796127 1
 
0.2%
Other values (520) 520
94.2%
(Missing) 17
 
3.1%
ValueCountFrequency (%)
126.9810447 1
0.2%
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%
ValueCountFrequency (%)
128.9006624 1
0.2%
128.1620994 1
0.2%
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%

계절
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
<NA>
532 
spring
 
6
summer
 
6
autumm
 
5
winter
 
3

Length

Max length6
Median length4
Mean length4.0724638
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 532
96.4%
spring 6
 
1.1%
summer 6
 
1.1%
autumm 5
 
0.9%
winter 3
 
0.5%

Length

2023-12-11T08:40:16.457945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:40:16.548250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 532
96.4%
spring 6
 
1.1%
summer 6
 
1.1%
autumm 5
 
0.9%
winter 3
 
0.5%

Interactions

2023-12-11T08:40:10.882721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:10.265852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:10.554400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:10.992274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:10.352520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:10.665774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:11.094938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:10.457417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:10.779652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:40:16.608989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조회수등록일위치홈페이지위도경도계절
조회수1.0000.8010.9631.0000.1560.1180.479
등록일0.8011.0000.9701.0000.5330.2410.467
위치0.9630.9701.0001.0000.9731.0001.000
홈페이지1.0001.0001.0001.0000.0000.000NaN
위도0.1560.5330.9730.0001.0000.9080.229
경도0.1180.2411.0000.0000.9081.0000.145
계절0.4790.4671.000NaN0.2290.1451.000
2023-12-11T08:40:16.694300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록일계절
등록일1.0000.172
계절0.1721.000
2023-12-11T08:40:16.762954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조회수위도경도등록일계절
조회수1.000-0.044-0.1910.4480.382
위도-0.0441.0000.0990.2930.190
경도-0.1910.0991.0000.1190.000
등록일0.4480.2930.1191.0000.172
계절0.3820.1900.0000.1721.000

Missing values

2023-12-11T08:40:11.240670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:40:11.433769image/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.
2023-12-11T08:40:11.575957image/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

제목조회수등록일주소위치홈페이지지도 주소값1위도경도계절
0臥山12522013-04-30노룡동 1번지사천시 사남면, 용현면, 벌용동, 남양동<NA>노룡동 1번지진삼로 40634.985498128.113926spring
1鳳鳴山7092013-04-30곤명면 용산리 86일원사천공항에서 22km<NA>사천시 곤명면 용산리 86다솔사길 41735.082917127.919893<NA>
2角山8392013-04-30사천시 동림동 190일원사천공항에서 18Km<NA>사천시 동림동 190수도골안길 2434.943346128.058944spring
3閑麗水道8232013-04-30늑도동 477 일원(초양휴게소)삼천포항 일원<NA>사천시 늑도동 477삼천포대교로 11234.925257128.045093<NA>
4南逸台海水浴場10712013-04-30향촌동 710 일원삼천포항에서 동쪽으로 3.5㎞<NA>향촌동 710남일대길 5534.926287128.096633summer
5象岩10812013-04-30향촌동 710 일원내삼천포항에서 동쪽으로 3.5Km<NA>향촌동 710남일대길 53-2434.925688128.097345summer
6大芳掘港11622013-04-30대방동 250삼천포 시외버스터미널에서 3Km<NA>대방동 250굴항길34.929053128.056777spring
7竹防簾8612013-04-30삼천포항 일대삼천포항(실안해안)<NA>동금동 579-7팔포3길 56-5134.925684128.079627winter
8三千浦港11502013-04-30서동 삼천포항 일대사천IC에서 22km<NA>서동 311-89어시장길 34-434.926532128.069654<NA>
9安海岸道路13932013-04-30실안동 1254일원실안해안도로 일원<NA>실안동 1254노을길34.938405128.043115autumm
제목조회수등록일주소위치홈페이지지도 주소값1위도경도계절
542주공칼국수282013-06-17동금동 62번지 14호<NA><NA>사천시 동금동 62번지 14호동금5길 3334.934123128.082374<NA>
543털보해물전골152013-06-17동동 173번지 27호<NA><NA>사천시 동동 173번지 27호수남3길 3134.928124128.070879<NA>
544풍년식당102013-06-17동금동 88번지 4호 경남상가 라동동 13<NA><NA>사천시 동금동 88번지 4호동금2길 1534.932122128.078632<NA>
545할매식당362013-06-17동금동 40번지 2호<NA><NA>사천시 동금동 40번지 2호남일로 9434.931771128.086544<NA>
546행운선지국밥172013-06-17동금동 88번지 4호 경남상가 다동 7호<NA><NA>사천시 동금동 88번지 4호동금2길 1534.932131128.078635<NA>
547향촌복집222013-06-17동금동 40번지 1호<NA><NA>사천시 동금동 40번지 1호남일로 94-134.931678128.08672<NA>
548洪川鍋き2432013-06-17동금동 336번지 5호<NA><NA>동금동 336번지 5호동금로 3034.930635128.078279<NA>
549황소막창782013-06-17동금동 65번지 5호<NA><NA>사천시 동금동 65번지 5호삼상로 42-134.934937128.08085<NA>
550모닝썬펜션342013-07-10송포동 1478번지<NA>http://www.rmorningsun.com/송포동 1478번지해안관광로 303-1534.960211128.03649<NA>
551씨&밸리펜션162013-07-10선구동 314-1<NA>http://www.씨엔밸리.kr/삼천포대교로 385-32삼천포대교로 385-3234.937725128.069484<NA>