Overview

Dataset statistics

Number of variables6
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.0 KiB
Average record size in memory51.3 B

Variable types

Text3
Numeric2
Categorical1

Alerts

ctprvn_cd is highly overall correlated with signgu_cd and 1 other fieldsHigh correlation
signgu_cd is highly overall correlated with ctprvn_cd and 1 other fieldsHigh correlation
ctprvn_nm is highly overall correlated with ctprvn_cd and 1 other fieldsHigh correlation
grp_nm has unique valuesUnique

Reproduction

Analysis started2023-12-10 09:59:46.991107
Analysis finished2023-12-10 09:59:48.840607
Duration1.85 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

grp_nm
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:59:49.054955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length19
Mean length12.79
Min length4

Characters and Unicode

Total characters1279
Distinct characters212
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row(사) 한국음악협회 청주지부
2nd row힐링아트센터
3rd row(사)SAK대구색동어머니회
4th row(사)가람통합예술교육연구소
5th row(사)가야금병창보존회
ValueCountFrequency (%)
사)경남민예총 4
 
3.3%
사)강원민예총 3
 
2.4%
사)국가무형문화재 3
 
2.4%
사)대한가수협회 2
 
1.6%
사 1
 
0.8%
사)국제장애인문화교류인천광역시협회 1
 
0.8%
힐링아트센터 1
 
0.8%
사)국제장애인문화교류전북협회 1
 
0.8%
사)국제키비탄한국본부 1
 
0.8%
사)군포프라임필하모닉오케스트라 1
 
0.8%
Other values (105) 105
85.4%
2023-12-10T18:59:49.708688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 98
 
7.7%
( 96
 
7.5%
) 96
 
7.5%
회 45
 
3.5%
국 33
 
2.6%
예 31
 
2.4%
화 27
 
2.1%
대 25
 
2.0%
문 24
 
1.9%
23
 
1.8%
Other values (202) 781
61.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1057
82.6%
Open Punctuation 96
 
7.5%
Close Punctuation 96
 
7.5%
Space Separator 23
 
1.8%
Uppercase Letter 3
 
0.2%
Other Punctuation 2
 
0.2%
Decimal Number 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 98
 
9.3%
회 45
 
4.3%
국 33
 
3.1%
예 31
 
2.9%
화 27
 
2.6%
대 25
 
2.4%
문 24
 
2.3%
구 22
 
2.1%
지 22
 
2.1%
부 21
 
2.0%
Other values (193) 709
67.1%
Uppercase Letter
ValueCountFrequency (%)
S 1
33.3%
A 1
33.3%
K 1
33.3%
Decimal Number
ValueCountFrequency (%)
3 1
50.0%
4 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 96
100.0%
Close Punctuation
ValueCountFrequency (%)
) 96
100.0%
Space Separator
ValueCountFrequency (%)
23
100.0%
Other Punctuation
ValueCountFrequency (%)
' 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1057
82.6%
Common 219
 
17.1%
Latin 3
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 98
 
9.3%
회 45
 
4.3%
국 33
 
3.1%
예 31
 
2.9%
화 27
 
2.6%
대 25
 
2.4%
문 24
 
2.3%
구 22
 
2.1%
지 22
 
2.1%
부 21
 
2.0%
Other values (193) 709
67.1%
Common
ValueCountFrequency (%)
( 96
43.8%
) 96
43.8%
23
 
10.5%
' 2
 
0.9%
3 1
 
0.5%
4 1
 
0.5%
Latin
ValueCountFrequency (%)
S 1
33.3%
A 1
33.3%
K 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1057
82.6%
ASCII 222
 
17.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 98
 
9.3%
회 45
 
4.3%
국 33
 
3.1%
예 31
 
2.9%
화 27
 
2.6%
대 25
 
2.4%
문 24
 
2.3%
구 22
 
2.1%
지 22
 
2.1%
부 21
 
2.0%
Other values (193) 709
67.1%
ASCII
ValueCountFrequency (%)
( 96
43.2%
) 96
43.2%
23
 
10.4%
' 2
 
0.9%
S 1
 
0.5%
A 1
 
0.5%
K 1
 
0.5%
3 1
 
0.5%
4 1
 
0.5%

locplc_dc
Text

Distinct56
Distinct (%)56.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:59:50.097748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.86
Min length5

Characters and Unicode

Total characters586
Distinct characters62
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

Unique33 ?
Unique (%)33.0%

Sample

1st row충북 청주시
2nd row서울 강남구
3rd row대구 수성구
4th row전북 전주시
5th row서울 서초구
ValueCountFrequency (%)
서울 24
 
12.0%
대구 11
 
5.5%
경남 10
 
5.0%
경기 8
 
4.0%
중구 8
 
4.0%
광주 8
 
4.0%
부산 7
 
3.5%
전북 6
 
3.0%
강원 6
 
3.0%
북구 6
 
3.0%
Other values (56) 106
53.0%
2023-12-10T18:59:50.728514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
100
17.1%
구 67
 
11.4%
시 38
 
6.5%
서 36
 
6.1%
울 24
 
4.1%
남 24
 
4.1%
경 23
 
3.9%
주 21
 
3.6%
대 20
 
3.4%
북 19
 
3.2%
Other values (52) 214
36.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 486
82.9%
Space Separator 100
 
17.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 67
 
13.8%
시 38
 
7.8%
서 36
 
7.4%
울 24
 
4.9%
남 24
 
4.9%
경 23
 
4.7%
주 21
 
4.3%
대 20
 
4.1%
북 19
 
3.9%
전 17
 
3.5%
Other values (51) 197
40.5%
Space Separator
ValueCountFrequency (%)
100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 486
82.9%
Common 100
 
17.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 67
 
13.8%
시 38
 
7.8%
서 36
 
7.4%
울 24
 
4.9%
남 24
 
4.9%
경 23
 
4.7%
주 21
 
4.3%
대 20
 
4.1%
북 19
 
3.9%
전 17
 
3.5%
Other values (51) 197
40.5%
Common
ValueCountFrequency (%)
100
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 486
82.9%
ASCII 100
 
17.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
100
100.0%
Hangul
ValueCountFrequency (%)
구 67
 
13.8%
시 38
 
7.8%
서 36
 
7.4%
울 24
 
4.9%
남 24
 
4.9%
경 23
 
4.7%
주 21
 
4.3%
대 20
 
4.1%
북 19
 
3.9%
전 17
 
3.5%
Other values (51) 197
40.5%

ctprvn_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.31
Minimum11
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:59:50.937834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q121
median24
Q334.25
95-th percentile38
Maximum39
Range28
Interquartile range (IQR)13.25

Descriptive statistics

Standard deviation9.8038284
Coefficient of variation (CV)0.38734999
Kurtosis-1.2962271
Mean25.31
Median Absolute Deviation (MAD)10
Skewness-0.26483147
Sum2531
Variance96.115051
MonotonicityNot monotonic
2023-12-10T18:59:51.202753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
11 24
24.0%
22 11
11.0%
38 10
10.0%
31 8
 
8.0%
24 8
 
8.0%
21 7
 
7.0%
35 6
 
6.0%
32 6
 
6.0%
33 4
 
4.0%
36 4
 
4.0%
Other values (5) 12
12.0%
ValueCountFrequency (%)
11 24
24.0%
21 7
 
7.0%
22 11
11.0%
23 1
 
1.0%
24 8
 
8.0%
25 4
 
4.0%
31 8
 
8.0%
32 6
 
6.0%
33 4
 
4.0%
34 2
 
2.0%
ValueCountFrequency (%)
39 2
 
2.0%
38 10
10.0%
37 3
 
3.0%
36 4
 
4.0%
35 6
6.0%
34 2
 
2.0%
33 4
 
4.0%
32 6
6.0%
31 8
8.0%
25 4
 
4.0%

ctprvn_nm
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울특별시
24 
대구광역시
11 
경상남도
10 
경기도
8 
광주광역시
8 
Other values (10)
39 

Length

Max length7
Median length5
Mean length4.47
Min length3

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row충청북도
2nd row서울특별시
3rd row대구광역시
4th row전라북도
5th row서울특별시

Common Values

ValueCountFrequency (%)
서울특별시 24
24.0%
대구광역시 11
11.0%
경상남도 10
10.0%
경기도 8
 
8.0%
광주광역시 8
 
8.0%
부산광역시 7
 
7.0%
전라북도 6
 
6.0%
강원도 6
 
6.0%
충청북도 4
 
4.0%
전라남도 4
 
4.0%
Other values (5) 12
12.0%

Length

2023-12-10T18:59:51.482083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울특별시 24
24.0%
대구광역시 11
11.0%
경상남도 10
10.0%
경기도 8
 
8.0%
광주광역시 8
 
8.0%
부산광역시 7
 
7.0%
전라북도 6
 
6.0%
강원도 6
 
6.0%
충청북도 4
 
4.0%
전라남도 4
 
4.0%
Other values (5) 12
12.0%

signgu_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)56.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25411.2
Minimum11010
Maximum39010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:59:51.769770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11010
5-th percentile11019.5
Q121050
median24050
Q334312.5
95-th percentile38110
Maximum39010
Range28000
Interquartile range (IQR)13262.5

Descriptive statistics

Standard deviation9804.2683
Coefficient of variation (CV)0.38582469
Kurtosis-1.3015182
Mean25411.2
Median Absolute Deviation (MAD)10010
Skewness-0.25538907
Sum2541120
Variance96123677
MonotonicityNot monotonic
2023-12-10T18:59:52.125729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11220 6
 
6.0%
38110 6
 
6.0%
22010 5
 
5.0%
11010 5
 
5.0%
11230 5
 
5.0%
24040 4
 
4.0%
35010 3
 
3.0%
32010 3
 
3.0%
33040 2
 
2.0%
37100 2
 
2.0%
Other values (46) 59
59.0%
ValueCountFrequency (%)
11010 5
5.0%
11020 2
 
2.0%
11050 1
 
1.0%
11060 1
 
1.0%
11130 2
 
2.0%
11190 1
 
1.0%
11200 1
 
1.0%
11220 6
6.0%
11230 5
5.0%
21020 1
 
1.0%
ValueCountFrequency (%)
39010 2
 
2.0%
38390 1
 
1.0%
38340 1
 
1.0%
38110 6
6.0%
38070 1
 
1.0%
38030 1
 
1.0%
37100 2
 
2.0%
37010 1
 
1.0%
36420 2
 
2.0%
36330 1
 
1.0%

signgu_nm
Text

Distinct51
Distinct (%)51.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:59:52.561678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.86
Min length2

Characters and Unicode

Total characters286
Distinct characters57
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

Unique29 ?
Unique (%)29.0%

Sample

1st row청주시
2nd row강남구
3rd row수성구
4th row전주시
5th row서초구
ValueCountFrequency (%)
중구 8
 
8.0%
북구 6
 
6.0%
창원시 6
 
6.0%
서초구 6
 
6.0%
강남구 5
 
5.0%
종로구 5
 
5.0%
전주시 3
 
3.0%
서구 3
 
3.0%
춘천시 3
 
3.0%
경산시 2
 
2.0%
Other values (41) 53
53.0%
2023-12-10T18:59:53.253381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 56
19.6%
시 38
 
13.3%
서 12
 
4.2%
주 11
 
3.8%
중 8
 
2.8%
산 8
 
2.8%
창 8
 
2.8%
남 8
 
2.8%
군 8
 
2.8%
초 7
 
2.4%
Other values (47) 122
42.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 286
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 56
19.6%
시 38
 
13.3%
서 12
 
4.2%
주 11
 
3.8%
중 8
 
2.8%
산 8
 
2.8%
창 8
 
2.8%
남 8
 
2.8%
군 8
 
2.8%
초 7
 
2.4%
Other values (47) 122
42.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 286
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 56
19.6%
시 38
 
13.3%
서 12
 
4.2%
주 11
 
3.8%
중 8
 
2.8%
산 8
 
2.8%
창 8
 
2.8%
남 8
 
2.8%
군 8
 
2.8%
초 7
 
2.4%
Other values (47) 122
42.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 286
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 56
19.6%
시 38
 
13.3%
서 12
 
4.2%
주 11
 
3.8%
중 8
 
2.8%
산 8
 
2.8%
창 8
 
2.8%
남 8
 
2.8%
군 8
 
2.8%
초 7
 
2.4%
Other values (47) 122
42.7%

Interactions

2023-12-10T18:59:48.187635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:47.834226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:48.359657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:59:48.021134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:59:53.459159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
grp_nmlocplc_dcctprvn_cdctprvn_nmsigngu_cdsigngu_nm
grp_nm1.0001.0001.0001.0001.0001.000
locplc_dc1.0001.0001.0001.0001.0001.000
ctprvn_cd1.0001.0001.0001.0000.9990.972
ctprvn_nm1.0001.0001.0001.0001.0000.994
signgu_cd1.0001.0000.9991.0001.0000.971
signgu_nm1.0001.0000.9720.9940.9711.000
2023-12-10T18:59:53.793859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ctprvn_cdsigngu_cdctprvn_nm
ctprvn_cd1.0000.9910.951
signgu_cd0.9911.0000.956
ctprvn_nm0.9510.9561.000

Missing values

2023-12-10T18:59:48.582984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:59:48.767119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

grp_nmlocplc_dcctprvn_cdctprvn_nmsigngu_cdsigngu_nm
0(사) 한국음악협회 청주지부충북 청주시33충청북도33040청주시
1힐링아트센터서울 강남구11서울특별시11230강남구
2(사)SAK대구색동어머니회대구 수성구22대구광역시22060수성구
3(사)가람통합예술교육연구소전북 전주시35전라북도35010전주시
4(사)가야금병창보존회서울 서초구11서울특별시11220서초구
5(사)가야금산조진흥회서울 서초구11서울특별시11220서초구
6(사)각설이품바보존회전남 무안군36전라남도36420무안군
7힐세라믹부산 서구21부산광역시21020서구
8(사)강원민예총강원 춘천시32강원도32010춘천시
9(사)강원민예총 속초지부강원 속초시32강원도32060속초시
grp_nmlocplc_dcctprvn_cdctprvn_nmsigngu_cdsigngu_nm
90(사)대구스트릿컬쳐팩토리대구 중구22대구광역시22010중구
91(사)대동문화재단광주 남구24광주광역시24030남구
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