Overview

Dataset statistics

Number of variables8
Number of observations216
Missing cells7
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.6 KiB
Average record size in memory64.6 B

Variable types

Categorical3
Text4
DateTime1

Dataset

Description도지정 무형문화재 데이터를 다루고 있는 자료로써 명칭, 주소, 선정자 성명 및 성별, 선정 분야에 관한 정보 현황입니다.
Author경상북도
URLhttps://www.data.go.kr/data/15071179/fileData.do

Alerts

신주소 has 3 (1.4%) missing valuesMissing

Reproduction

Analysis started2023-12-23 07:46:29.384538
Analysis finished2023-12-23 07:46:35.466411
Duration6.08 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

명 칭
Categorical

Distinct50
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
청도 차산농악
25 
예천공처농요
14 
가야금병창
 
13
상주민요
 
13
안동놋다리밟기
 
12
Other values (45)
139 

Length

Max length9
Median length8
Mean length6.1990741
Min length2

Unique

Unique19 ?
Unique (%)8.8%

Sample

1st row안동포짜기
2nd row안동포짜기
3rd row안동포짜기
4th row안동포짜기
5th row안동포짜기

Common Values

ValueCountFrequency (%)
청도 차산농악 25
 
11.6%
예천공처농요 14
 
6.5%
가야금병창 13
 
6.0%
상주민요 13
 
6.0%
안동놋다리밟기 12
 
5.6%
영덕 월월이청청 12
 
5.6%
영덕별신굿 10
 
4.6%
구미발갱이들소리 9
 
4.2%
안동저전동농요 9
 
4.2%
자인계정들소리 8
 
3.7%
Other values (40) 91
42.1%

Length

2023-12-23T07:46:35.836886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
청도 30
 
10.1%
차산농악 25
 
8.4%
예천공처농요 14
 
4.7%
영덕 14
 
4.7%
가야금병창 13
 
4.4%
상주민요 13
 
4.4%
안동놋다리밟기 12
 
4.0%
월월이청청 12
 
4.0%
영덕별신굿 10
 
3.4%
구미발갱이들소리 9
 
3.0%
Other values (54) 146
49.0%

구 분
Categorical

Distinct6
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
전수장학생
114 
전승교육사
54 
보유자
33 
보유단체대표
13 
전승교육사
 
1

Length

Max length6
Median length5
Mean length4.7592593
Min length3

Unique

Unique2 ?
Unique (%)0.9%

Sample

1st row보유자
2nd row보유자
3rd row전승교육사
4th row전승교육사
5th row전수장학생

Common Values

ValueCountFrequency (%)
전수장학생 114
52.8%
전승교육사 54
25.0%
보유자 33
 
15.3%
보유단체대표 13
 
6.0%
전승교육사 1
 
0.5%
공동체종목 1
 
0.5%

Length

2023-12-23T07:46:36.569287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-23T07:46:37.189931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전수장학생 114
52.8%
전승교육사 55
25.5%
보유자 33
 
15.3%
보유단체대표 13
 
6.0%
공동체종목 1
 
0.5%

성 명
Text

Distinct209
Distinct (%)97.2%
Missing1
Missing (%)0.5%
Memory size1.8 KiB
2023-12-23T07:46:38.768378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters645
Distinct characters145
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique203 ?
Unique (%)94.4%

Sample

1st row우복인
2nd row권연이
3rd row정복영
4th row조경숙
5th row김춘화
ValueCountFrequency (%)
정소라 2
 
0.9%
조석탑 2
 
0.9%
김경희 2
 
0.9%
박광식 2
 
0.9%
조일환 2
 
0.9%
양주석 2
 
0.9%
김희대 1
 
0.5%
정은주 1
 
0.5%
이수일 1
 
0.5%
신지선 1
 
0.5%
Other values (199) 199
92.6%
2023-12-23T07:46:41.057404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 60
 
9.3%
정 35
 
5.4%
이 28
 
4.3%
영 22
 
3.4%
순 15
 
2.3%
조 13
 
2.0%
경 12
 
1.9%
주 11
 
1.7%
희 11
 
1.7%
호 11
 
1.7%
Other values (135) 427
66.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 645
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 60
 
9.3%
정 35
 
5.4%
이 28
 
4.3%
영 22
 
3.4%
순 15
 
2.3%
조 13
 
2.0%
경 12
 
1.9%
주 11
 
1.7%
희 11
 
1.7%
호 11
 
1.7%
Other values (135) 427
66.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 645
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 60
 
9.3%
정 35
 
5.4%
이 28
 
4.3%
영 22
 
3.4%
순 15
 
2.3%
조 13
 
2.0%
경 12
 
1.9%
주 11
 
1.7%
희 11
 
1.7%
호 11
 
1.7%
Other values (135) 427
66.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 645
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 60
 
9.3%
정 35
 
5.4%
이 28
 
4.3%
영 22
 
3.4%
순 15
 
2.3%
조 13
 
2.0%
경 12
 
1.9%
주 11
 
1.7%
희 11
 
1.7%
호 11
 
1.7%
Other values (135) 427
66.2%

성별
Categorical

Distinct4
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
남
108 
여
106 
남
 
1
<NA>
 
1

Length

Max length4
Median length1
Mean length1.0185185
Min length1

Unique

Unique2 ?
Unique (%)0.9%

Sample

1st row여
2nd row여
3rd row여
4th row여
5th row여

Common Values

ValueCountFrequency (%)
남 108
50.0%
여 106
49.1%
남 1
 
0.5%
<NA> 1
 
0.5%

Length

2023-12-23T07:46:42.093123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-23T07:46:42.766470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남 109
50.5%
여 106
49.1%
na 1
 
0.5%

생 년
Text

Distinct67
Distinct (%)31.2%
Missing1
Missing (%)0.5%
Memory size1.8 KiB
2023-12-23T07:46:43.865170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.0046512
Min length4

Characters and Unicode

Total characters861
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)6.0%

Sample

1st row1932
2nd row1943
3rd row1942
4th row1956
5th row1955
ValueCountFrequency (%)
1956 14
 
6.5%
1960 9
 
4.2%
1973 8
 
3.7%
1961 6
 
2.8%
1955 6
 
2.8%
1958 6
 
2.8%
1968 6
 
2.8%
1964 6
 
2.8%
1957 6
 
2.8%
1959 5
 
2.3%
Other values (57) 143
66.5%
2023-12-23T07:46:45.724740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 243
28.2%
1 226
26.2%
6 78
 
9.1%
5 76
 
8.8%
7 51
 
5.9%
4 44
 
5.1%
0 43
 
5.0%
8 35
 
4.1%
3 32
 
3.7%
2 32
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 860
99.9%
Other Letter 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 243
28.3%
1 226
26.3%
6 78
 
9.1%
5 76
 
8.8%
7 51
 
5.9%
4 44
 
5.1%
0 43
 
5.0%
8 35
 
4.1%
3 32
 
3.7%
2 32
 
3.7%
Other Letter
ValueCountFrequency (%)
년 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 860
99.9%
Hangul 1
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
9 243
28.3%
1 226
26.3%
6 78
 
9.1%
5 76
 
8.8%
7 51
 
5.9%
4 44
 
5.1%
0 43
 
5.0%
8 35
 
4.1%
3 32
 
3.7%
2 32
 
3.7%
Hangul
ValueCountFrequency (%)
년 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 860
99.9%
Hangul 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 243
28.3%
1 226
26.3%
6 78
 
9.1%
5 76
 
8.8%
7 51
 
5.9%
4 44
 
5.1%
0 43
 
5.0%
8 35
 
4.1%
3 32
 
3.7%
2 32
 
3.7%
Hangul
ValueCountFrequency (%)
년 1
100.0%

분 야
Text

Distinct58
Distinct (%)27.0%
Missing1
Missing (%)0.5%
Memory size1.8 KiB
2023-12-23T07:46:46.579397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.9162791
Min length1

Characters and Unicode

Total characters627
Distinct characters77
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)8.8%

Sample

1st row포짜기
2nd row포짜기
3rd row포짜기
4th row포짜기
5th row포짜기
ValueCountFrequency (%)
선소리 15
 
6.3%
가야금병창 13
 
5.5%
술담기 12
 
5.1%
사기장 11
 
4.6%
민 9
 
3.8%
요 9
 
3.8%
민속음악 9
 
3.8%
춤 8
 
3.4%
장구 8
 
3.4%
모심기 7
 
3.0%
Other values (49) 136
57.4%
2023-12-23T07:46:48.581807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 59
 
9.4%
기 52
 
8.3%
소 37
 
5.9%
리 37
 
5.9%
22
 
3.5%
민 21
 
3.3%
사 18
 
2.9%
창 17
 
2.7%
선 16
 
2.6%
가 15
 
2.4%
Other values (67) 333
53.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 597
95.2%
Space Separator 28
 
4.5%
Other Punctuation 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 59
 
9.9%
기 52
 
8.7%
소 37
 
6.2%
리 37
 
6.2%
민 21
 
3.5%
사 18
 
3.0%
창 17
 
2.8%
선 16
 
2.7%
가 15
 
2.5%
악 14
 
2.3%
Other values (63) 311
52.1%
Space Separator
ValueCountFrequency (%)
22
78.6%
　 6
 
21.4%
Other Punctuation
ValueCountFrequency (%)
· 1
50.0%
/ 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 597
95.2%
Common 30
 
4.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 59
 
9.9%
기 52
 
8.7%
소 37
 
6.2%
리 37
 
6.2%
민 21
 
3.5%
사 18
 
3.0%
창 17
 
2.8%
선 16
 
2.7%
가 15
 
2.5%
악 14
 
2.3%
Other values (63) 311
52.1%
Common
ValueCountFrequency (%)
22
73.3%
　 6
 
20.0%
· 1
 
3.3%
/ 1
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 597
95.2%
ASCII 23
 
3.7%
None 7
 
1.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 59
 
9.9%
기 52
 
8.7%
소 37
 
6.2%
리 37
 
6.2%
민 21
 
3.5%
사 18
 
3.0%
창 17
 
2.8%
선 16
 
2.7%
가 15
 
2.5%
악 14
 
2.3%
Other values (63) 311
52.1%
ASCII
ValueCountFrequency (%)
22
95.7%
/ 1
 
4.3%
None
ValueCountFrequency (%)
　 6
85.7%
· 1
 
14.3%

신주소
Text

MISSING 

Distinct121
Distinct (%)56.8%
Missing3
Missing (%)1.4%
Memory size1.8 KiB
2023-12-23T07:46:49.900828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length7
Mean length7.2629108
Min length6

Characters and Unicode

Total characters1547
Distinct characters133
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86 ?
Unique (%)40.4%

Sample

1st row안동시 임하면
2nd row안동시 임하면
3rd row안동시 임하면
4th row안동시 임하면
5th row안동시 임하면
ValueCountFrequency (%)
안동시 33
 
7.7%
청도군 24
 
5.6%
경주시 20
 
4.6%
경산시 17
 
3.9%
영덕군 17
 
3.9%
예천군 16
 
3.7%
문경시 15
 
3.5%
상주시 15
 
3.5%
풍양면 14
 
3.2%
구미시 13
 
3.0%
Other values (130) 247
57.3%
2023-12-23T07:46:51.852316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
229
 
14.8%
시 132
 
8.5%
면 96
 
6.2%
군 79
 
5.1%
경 60
 
3.9%
안 47
 
3.0%
길 46
 
3.0%
동 45
 
2.9%
읍 44
 
2.8%
산 42
 
2.7%
Other values (123) 727
47.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1291
83.5%
Space Separator 229
 
14.8%
Decimal Number 27
 
1.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 132
 
10.2%
면 96
 
7.4%
군 79
 
6.1%
경 60
 
4.6%
안 47
 
3.6%
길 46
 
3.6%
동 45
 
3.5%
읍 44
 
3.4%
산 42
 
3.3%
주 38
 
2.9%
Other values (117) 662
51.3%
Decimal Number
ValueCountFrequency (%)
1 14
51.9%
3 5
 
18.5%
5 4
 
14.8%
2 3
 
11.1%
8 1
 
3.7%
Space Separator
ValueCountFrequency (%)
229
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1291
83.5%
Common 256
 
16.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 132
 
10.2%
면 96
 
7.4%
군 79
 
6.1%
경 60
 
4.6%
안 47
 
3.6%
길 46
 
3.6%
동 45
 
3.5%
읍 44
 
3.4%
산 42
 
3.3%
주 38
 
2.9%
Other values (117) 662
51.3%
Common
ValueCountFrequency (%)
229
89.5%
1 14
 
5.5%
3 5
 
2.0%
5 4
 
1.6%
2 3
 
1.2%
8 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1290
83.4%
ASCII 256
 
16.5%
Compat Jamo 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
229
89.5%
1 14
 
5.5%
3 5
 
2.0%
5 4
 
1.6%
2 3
 
1.2%
8 1
 
0.4%
Hangul
ValueCountFrequency (%)
시 132
 
10.2%
면 96
 
7.4%
군 79
 
6.1%
경 60
 
4.7%
안 47
 
3.6%
길 46
 
3.6%
동 45
 
3.5%
읍 44
 
3.4%
산 42
 
3.3%
주 38
 
2.9%
Other values (116) 661
51.2%
Compat Jamo
ValueCountFrequency (%)
ㅂ 1
100.0%

인(선)정일자
Date

Distinct66
Distinct (%)30.7%
Missing1
Missing (%)0.5%
Memory size1.8 KiB
Minimum1989-05-29 00:00:00
Maximum2023-12-14 00:00:00
2023-12-23T07:46:52.411118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:46:53.030890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Correlations

2023-12-23T07:46:53.421756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
명 칭구 분성별생 년분 야인(선)정일자
명 칭1.0000.8130.5200.0000.9940.969
구 분0.8131.0000.2240.0000.8360.767
성별0.5200.2241.0000.0000.5450.000
생 년0.0000.0000.0001.0000.0000.690
분 야0.9940.8360.5450.0001.0000.919
인(선)정일자0.9690.7670.0000.6900.9191.000
2023-12-23T07:46:54.016765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
명 칭성별구 분
명 칭1.0000.2640.454
성별0.2641.0000.171
구 분0.4540.1711.000
2023-12-23T07:46:54.571523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
명 칭구 분성별
명 칭1.0000.4540.264
구 분0.4541.0000.171
성별0.2640.1711.000

Missing values

2023-12-23T07:46:33.314511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-23T07:46:34.240716image/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.
2023-12-23T07:46:35.027132image/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

명 칭구 분성 명성별생 년분 야신주소인(선)정일자
0안동포짜기보유자우복인여1932포짜기안동시 임하면2006-05-18
1안동포짜기보유자권연이여1943포짜기안동시 임하면2018-10-18
2안동포짜기전승교육사정복영여1942포짜기안동시 임하면1995-06-30
3안동포짜기전승교육사조경숙여1956포짜기안동시 임하면2022-11-14
4안동포짜기전수장학생김춘화여1955포짜기안동시 임하면2022-11-14
5안동포짜기전승교육사김순호여1956포짜기안동시 임하면2022-11-14
6안동저전동농요전승교육사조석탑남1947선소리안동시 서후면1995-06-30
7안동저전동농요전승교육사조광용남1941칭칭이안동시 서후면1996-12-31
8안동저전동농요전수장학생김필용여1955모심기안동시 서후면2018-10-18
9안동저전동농요전수장학생김순낭여1952모심기안동시 서후면2018-10-18
명 칭구 분성 명성별생 년분 야신주소인(선)정일자
206영천 목조각장전수장학생김광언남1971목조각장경주시 서면2019-07-05
207영천 목조각장전수장학생신환수남1982목조각장경주시 서악1길2021-05-17
208영천 목조각장전수장학생김복연남1964목조각장의성군 춘산면2023-06-29
209문경 모전들소리보유단체대표김제수남1958민속음악문경시 모전동2020-04-09
210칠곡 석장보유자남진세남1956석장칠곡군 동명면2021-08-26
211고령 제와장보유자김은동남1949제와장고령군 개진면2023-02-16
212경주 석장보유자윤만걸남1954석장경주시 남산면2023-02-16
213영덕 토염공동체종목<NA><NA><NA><NA><NA>2023-06-29
214경주 숭덕전제례보유단체대표박낙규남1953제례의식<NA>2023-11-06
215포항 침선장보유자조정화여1956침선장<NA>2023-12-14