Overview

Dataset statistics

Number of variables6
Number of observations255
Missing cells254
Missing cells (%)16.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.3 KiB
Average record size in memory49.5 B

Variable types

Numeric1
Text3
DateTime2

Dataset

Description2017년 10월기준 경상남도 기념물 현황입니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15056111

Alerts

추가지정일 has 253 (99.2%) missing valuesMissing
지정번호 has unique valuesUnique
명칭 has unique valuesUnique

Reproduction

Analysis started2023-12-11 00:54:11.529516
Analysis finished2023-12-11 00:54:12.149764
Duration0.62 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지정번호
Real number (ℝ)

UNIQUE 

Distinct255
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean149.54118
Minimum1
Maximum288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2023-12-11T09:54:12.221165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15.7
Q180.5
median154
Q3222.5
95-th percentile275.3
Maximum288
Range287
Interquartile range (IQR)142

Descriptive statistics

Standard deviation83.608605
Coefficient of variation (CV)0.55910089
Kurtosis-1.1748852
Mean149.54118
Median Absolute Deviation (MAD)71
Skewness-0.099354604
Sum38133
Variance6990.3989
MonotonicityStrictly increasing
2023-12-11T09:54:12.369150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.4%
2 1
 
0.4%
190 1
 
0.4%
191 1
 
0.4%
192 1
 
0.4%
193 1
 
0.4%
195 1
 
0.4%
196 1
 
0.4%
197 1
 
0.4%
198 1
 
0.4%
Other values (245) 245
96.1%
ValueCountFrequency (%)
1 1
0.4%
2 1
0.4%
3 1
0.4%
4 1
0.4%
5 1
0.4%
6 1
0.4%
7 1
0.4%
8 1
0.4%
9 1
0.4%
10 1
0.4%
ValueCountFrequency (%)
288 1
0.4%
287 1
0.4%
286 1
0.4%
285 1
0.4%
284 1
0.4%
283 1
0.4%
282 1
0.4%
281 1
0.4%
280 1
0.4%
279 1
0.4%

명칭
Text

UNIQUE 

Distinct255
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2023-12-11T09:54:12.596632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length7.3764706
Min length3

Characters and Unicode

Total characters1881
Distinct characters250
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique255 ?
Unique (%)100.0%

Sample

1st row진주옥봉고분군
2nd row창녕지석묘
3rd row계성고분군
4th row서상동 지석묘
5th row창원외동지석묘
ValueCountFrequency (%)
의령 10
 
2.9%
통영 10
 
2.9%
진주 7
 
2.0%
묘 5
 
1.5%
봉수대 4
 
1.2%
거제 4
 
1.2%
전 3
 
0.9%
지석묘 3
 
0.9%
김해 3
 
0.9%
고분군 3
 
0.9%
Other values (284) 291
84.8%
2023-12-11T09:54:12.913818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 89
 
4.7%
88
 
4.7%
지 87
 
4.6%
성 73
 
3.9%
리 69
 
3.7%
사 50
 
2.7%
묘 47
 
2.5%
천 40
 
2.1%
무 38
 
2.0%
군 37
 
2.0%
Other values (240) 1263
67.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1785
94.9%
Space Separator 88
 
4.7%
Other Punctuation 4
 
0.2%
Decimal Number 4
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 89
 
5.0%
지 87
 
4.9%
성 73
 
4.1%
리 69
 
3.9%
사 50
 
2.8%
묘 47
 
2.6%
천 40
 
2.2%
무 38
 
2.1%
군 37
 
2.1%
대 37
 
2.1%
Other values (234) 1218
68.2%
Decimal Number
ValueCountFrequency (%)
4 1
25.0%
3 1
25.0%
1 1
25.0%
7 1
25.0%
Space Separator
ValueCountFrequency (%)
88
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1785
94.9%
Common 96
 
5.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 89
 
5.0%
지 87
 
4.9%
성 73
 
4.1%
리 69
 
3.9%
사 50
 
2.8%
묘 47
 
2.6%
천 40
 
2.2%
무 38
 
2.1%
군 37
 
2.1%
대 37
 
2.1%
Other values (234) 1218
68.2%
Common
ValueCountFrequency (%)
88
91.7%
. 4
 
4.2%
4 1
 
1.0%
3 1
 
1.0%
1 1
 
1.0%
7 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1785
94.9%
ASCII 96
 
5.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 89
 
5.0%
지 87
 
4.9%
성 73
 
4.1%
리 69
 
3.9%
사 50
 
2.8%
묘 47
 
2.6%
천 40
 
2.2%
무 38
 
2.1%
군 37
 
2.1%
대 37
 
2.1%
Other values (234) 1218
68.2%
ASCII
ValueCountFrequency (%)
88
91.7%
. 4
 
4.2%
4 1
 
1.0%
3 1
 
1.0%
1 1
 
1.0%
7 1
 
1.0%

수량면적
Text

Distinct215
Distinct (%)84.6%
Missing1
Missing (%)0.4%
Memory size2.1 KiB
2023-12-11T09:54:13.109149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length4.2755906
Min length1

Characters and Unicode

Total characters1086
Distinct characters14
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

Unique207 ?
Unique (%)81.5%

Sample

1st row97
2nd row267
3rd row136694
4th row1126
5th row111.4
ValueCountFrequency (%)
1 29
 
11.4%
2 5
 
2.0%
5 3
 
1.2%
6 2
 
0.8%
23679 2
 
0.8%
3223 2
 
0.8%
400 2
 
0.8%
4 2
 
0.8%
1430 1
 
0.4%
24497 1
 
0.4%
Other values (205) 205
80.7%
2023-12-11T09:54:13.459024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 193
17.8%
2 125
11.5%
0 112
10.3%
9 110
10.1%
3 100
9.2%
4 94
8.7%
5 82
7.6%
7 81
7.5%
6 73
 
6.7%
8 64
 
5.9%
Other values (4) 52
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1034
95.2%
Other Punctuation 39
 
3.6%
Open Punctuation 6
 
0.6%
Close Punctuation 6
 
0.6%
Other Letter 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 193
18.7%
2 125
12.1%
0 112
10.8%
9 110
10.6%
3 100
9.7%
4 94
9.1%
5 82
7.9%
7 81
7.8%
6 73
 
7.1%
8 64
 
6.2%
Other Punctuation
ValueCountFrequency (%)
. 39
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Other Letter
ValueCountFrequency (%)
여 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1085
99.9%
Hangul 1
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 193
17.8%
2 125
11.5%
0 112
10.3%
9 110
10.1%
3 100
9.2%
4 94
8.7%
5 82
7.6%
7 81
7.5%
6 73
 
6.7%
8 64
 
5.9%
Other values (3) 51
 
4.7%
Hangul
ValueCountFrequency (%)
여 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1085
99.9%
Hangul 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 193
17.8%
2 125
11.5%
0 112
10.3%
9 110
10.1%
3 100
9.2%
4 94
8.7%
5 82
7.6%
7 81
7.5%
6 73
 
6.7%
8 64
 
5.9%
Other values (3) 51
 
4.7%
Hangul
ValueCountFrequency (%)
여 1
100.0%

소재지
Text

Distinct153
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2023-12-11T09:54:13.798061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length7.0039216
Min length6

Characters and Unicode

Total characters1786
Distinct characters123
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique98 ?
Unique (%)38.4%

Sample

1st row진주시 옥봉남동
2nd row창녕군 장마면
3rd row창녕군 계성면
4th row김해시 서상동
5th row창원시 성산구
ValueCountFrequency (%)
창원시 31
 
6.1%
거제시 25
 
4.9%
사천시 16
 
3.1%
진주시 16
 
3.1%
합천군 16
 
3.1%
산청군 15
 
2.9%
밀양시 15
 
2.9%
남해군 14
 
2.7%
김해시 13
 
2.5%
의령군 13
 
2.5%
Other values (163) 336
65.9%
2023-12-11T09:54:14.248586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
255
 
14.3%
면 168
 
9.4%
시 137
 
7.7%
군 120
 
6.7%
창 58
 
3.2%
동 56
 
3.1%
산 42
 
2.4%
천 41
 
2.3%
해 40
 
2.2%
양 39
 
2.2%
Other values (113) 830
46.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1530
85.7%
Space Separator 255
 
14.3%
Decimal Number 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
면 168
 
11.0%
시 137
 
9.0%
군 120
 
7.8%
창 58
 
3.8%
동 56
 
3.7%
산 42
 
2.7%
천 41
 
2.7%
해 40
 
2.6%
양 39
 
2.5%
거 36
 
2.4%
Other values (111) 793
51.8%
Space Separator
ValueCountFrequency (%)
255
100.0%
Decimal Number
ValueCountFrequency (%)
4 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1530
85.7%
Common 256
 
14.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
면 168
 
11.0%
시 137
 
9.0%
군 120
 
7.8%
창 58
 
3.8%
동 56
 
3.7%
산 42
 
2.7%
천 41
 
2.7%
해 40
 
2.6%
양 39
 
2.5%
거 36
 
2.4%
Other values (111) 793
51.8%
Common
ValueCountFrequency (%)
255
99.6%
4 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1530
85.7%
ASCII 256
 
14.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
255
99.6%
4 1
 
0.4%
Hangul
ValueCountFrequency (%)
면 168
 
11.0%
시 137
 
9.0%
군 120
 
7.8%
창 58
 
3.8%
동 56
 
3.7%
산 42
 
2.7%
천 41
 
2.7%
해 40
 
2.6%
양 39
 
2.5%
거 36
 
2.4%
Other values (111) 793
51.8%

지정일(확대지정)
Date

Distinct76
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
Minimum1974-02-16 00:00:00
Maximum2017-07-13 00:00:00
2023-12-11T09:54:14.383875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:54:14.749481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

추가지정일
Date

MISSING 

Distinct2
Distinct (%)100.0%
Missing253
Missing (%)99.2%
Memory size2.1 KiB
Minimum1997-01-30 00:00:00
Maximum1997-12-31 00:00:00
2023-12-11T09:54:14.840231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:54:14.917798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

Interactions

2023-12-11T09:54:11.812737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:54:14.986674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정번호지정일(확대지정)추가지정일
지정번호1.0000.9980.000
지정일(확대지정)0.9981.0000.000
추가지정일0.0000.0001.000

Missing values

2023-12-11T09:54:11.911974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:54:12.001117image/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.
2023-12-11T09:54:12.106439image/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

지정번호명칭수량면적소재지지정일(확대지정)추가지정일
01진주옥봉고분군97진주시 옥봉남동1974-02-16<NA>
12창녕지석묘267창녕군 장마면1974-02-16<NA>
23계성고분군136694창녕군 계성면1974-02-16<NA>
34서상동 지석묘1126김해시 서상동1974-02-16<NA>
45창원외동지석묘111.4창원시 성산구1974-02-16<NA>
56남해상주리석각1남해시 상주면1974-02-16<NA>
67산청생초고분군26234산청군 생초면1974-02-16<NA>
78삼가고분군78149합천군 삼가면1974-02-16<NA>
89사등성지42118거제시 사등면1974-02-16<NA>
910옥산성지18157거제시 거제면1974-02-16<NA>
지정번호명칭수량면적소재지지정일(확대지정)추가지정일
245279통영 우산 봉수1(242)통영시 도산면2011-12-29<NA>
246280김해 구산동 지석묘1(4660.1)김해시 구산동2012-07-19<NA>
247281밀양 처자교1(14677)밀양시 삼랑진읍2012-08-30<NA>
248282창녕 창성부원군 조민수의 묘<NA>창녕군 대합면2013-07-13<NA>
249283함안 조려 묘1(147)함안군 법수면2015-03-19<NA>
250284진주 이정 묘112진주시 정촌면2016-08-25<NA>
251285남해 전 선원사지3971남해군 고현면2017-03-02<NA>
252286남해 전 백련암지6359남해군 고현면2017-03-02<NA>
253287김해 장군차 서식지90013980김해시 동상동2017-06-29<NA>
254288김해 상동 분청사기 가마터5866김해시 대감리2017-07-13<NA>