Overview

Dataset statistics

Number of variables14
Number of observations37
Missing cells296
Missing cells (%)57.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.5 KiB
Average record size in memory125.6 B

Variable types

Categorical3
Numeric2
Text1
Unsupported8

Dataset

Description소재지대륙명,소재지국가명,소재지도시명,SEQNO,기구명,가입연도,설립목적,설립연혁,회원도시,회원자격,회비,회의주기,주요활동,사무국담당자
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-2489/S/1/datasetView.do

Alerts

소재지국가명 is highly overall correlated with 소재지대륙명 and 1 other fieldsHigh correlation
소재지도시명 is highly overall correlated with 소재지대륙명 and 1 other fieldsHigh correlation
소재지대륙명 is highly overall correlated with 소재지국가명 and 1 other fieldsHigh correlation
설립목적 has 37 (100.0%) missing valuesMissing
설립연혁 has 37 (100.0%) missing valuesMissing
회원도시 has 37 (100.0%) missing valuesMissing
회원자격 has 37 (100.0%) missing valuesMissing
회비 has 37 (100.0%) missing valuesMissing
회의주기 has 37 (100.0%) missing valuesMissing
주요활동 has 37 (100.0%) missing valuesMissing
사무국담당자 has 37 (100.0%) missing valuesMissing
SEQNO has unique valuesUnique
기구명 has unique valuesUnique
설립목적 is an unsupported type, check if it needs cleaning or further analysisUnsupported
설립연혁 is an unsupported type, check if it needs cleaning or further analysisUnsupported
회원도시 is an unsupported type, check if it needs cleaning or further analysisUnsupported
회원자격 is an unsupported type, check if it needs cleaning or further analysisUnsupported
회비 is an unsupported type, check if it needs cleaning or further analysisUnsupported
회의주기 is an unsupported type, check if it needs cleaning or further analysisUnsupported
주요활동 is an unsupported type, check if it needs cleaning or further analysisUnsupported
사무국담당자 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-29 21:49:09.401316
Analysis finished2024-04-29 21:49:11.915879
Duration2.51 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

소재지대륙명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Memory size428.0 B
아시아
16 
유럽
15 
북아메리카
6 

Length

Max length5
Median length3
Mean length2.9189189
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row북아메리카
2nd row유럽
3rd row유럽
4th row아시아
5th row북아메리카

Common Values

ValueCountFrequency (%)
아시아 16
43.2%
유럽 15
40.5%
북아메리카 6
 
16.2%

Length

2024-04-30T06:49:11.976373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T06:49:12.067835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
아시아 16
43.2%
유럽 15
40.5%
북아메리카 6
 
16.2%

소재지국가명
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Memory size428.0 B
한국
14 
미국
5 
스위스
5 
이탈리아
2 
독일
2 
Other values (7)
9 

Length

Max length5
Median length2
Mean length2.5945946
Min length2

Unique

Unique5 ?
Unique (%)13.5%

Sample

1st row미국
2nd row이탈리아
3rd row스위스
4th row한국
5th row미국

Common Values

ValueCountFrequency (%)
한국 14
37.8%
미국 5
 
13.5%
스위스 5
 
13.5%
이탈리아 2
 
5.4%
독일 2
 
5.4%
스페인 2
 
5.4%
프랑스 2
 
5.4%
인도네시아 1
 
2.7%
캐나다 1
 
2.7%
싱가포르 1
 
2.7%
Other values (2) 2
 
5.4%

Length

2024-04-30T06:49:12.178035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한국 14
37.8%
미국 5
 
13.5%
스위스 5
 
13.5%
이탈리아 2
 
5.4%
독일 2
 
5.4%
스페인 2
 
5.4%
프랑스 2
 
5.4%
인도네시아 1
 
2.7%
캐나다 1
 
2.7%
싱가포르 1
 
2.7%
Other values (2) 2
 
5.4%

소재지도시명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)35.1%
Missing0
Missing (%)0.0%
Memory size428.0 B
서울
14 
뉴욕
5 
제네바
4 
로마
2 
본
2 
Other values (8)
10 

Length

Max length5
Median length2
Mean length2.4054054
Min length1

Unique

Unique6 ?
Unique (%)16.2%

Sample

1st row뉴욕
2nd row로마
3rd row제네바
4th row서울
5th row뉴욕

Common Values

ValueCountFrequency (%)
서울 14
37.8%
뉴욕 5
 
13.5%
제네바 4
 
10.8%
로마 2
 
5.4%
본 2
 
5.4%
바르셀로나 2
 
5.4%
파리 2
 
5.4%
자카르타 1
 
2.7%
몬트리올 1
 
2.7%
기타 1
 
2.7%
Other values (3) 3
 
8.1%

Length

2024-04-30T06:49:12.308877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울 14
37.8%
뉴욕 5
 
13.5%
제네바 4
 
10.8%
로마 2
 
5.4%
본 2
 
5.4%
바르셀로나 2
 
5.4%
파리 2
 
5.4%
자카르타 1
 
2.7%
몬트리올 1
 
2.7%
기타 1
 
2.7%
Other values (3) 3
 
8.1%

SEQNO
Real number (ℝ)

UNIQUE 

Distinct37
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.783784
Minimum10
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size465.0 B
2024-04-30T06:49:12.441382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile20.6
Q133
median42
Q351
95-th percentile58.2
Maximum60
Range50
Interquartile range (IQR)18

Descriptive statistics

Standard deviation12.957484
Coefficient of variation (CV)0.31771167
Kurtosis-0.19913483
Mean40.783784
Median Absolute Deviation (MAD)9
Skewness-0.55823664
Sum1509
Variance167.8964
MonotonicityNot monotonic
2024-04-30T06:49:12.562131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
60 1
 
2.7%
38 1
 
2.7%
27 1
 
2.7%
39 1
 
2.7%
41 1
 
2.7%
51 1
 
2.7%
40 1
 
2.7%
49 1
 
2.7%
50 1
 
2.7%
36 1
 
2.7%
Other values (27) 27
73.0%
ValueCountFrequency (%)
10 1
2.7%
11 1
2.7%
23 1
2.7%
24 1
2.7%
25 1
2.7%
26 1
2.7%
27 1
2.7%
30 1
2.7%
31 1
2.7%
33 1
2.7%
ValueCountFrequency (%)
60 1
2.7%
59 1
2.7%
58 1
2.7%
57 1
2.7%
56 1
2.7%
55 1
2.7%
54 1
2.7%
53 1
2.7%
52 1
2.7%
51 1
2.7%

기구명
Text

UNIQUE 

Distinct37
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size428.0 B
2024-04-30T06:49:12.796186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length26
Mean length23
Min length8

Characters and Unicode

Total characters851
Distinct characters180
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)100.0%

Sample

1st row유엔인구기금 한국사무소 (UNPFA)
2nd row유엔식량농업기구 협력연락사무소(FAO)
3rd row세계보건기구(WHO) 아태환경보건센터
4th row아셈노인인권정책센터(AGAC)
5th row휴먼라이츠워치 한국지부(HRW Korea)
ValueCountFrequency (%)
korea 5
 
4.3%
유엔 3
 
2.6%
국제 3
 
2.6%
seoul 2
 
1.7%
office 2
 
1.7%
아시아 2
 
1.7%
메트로폴리스 2
 
1.7%
한국사무소 2
 
1.7%
아시아정당 1
 
0.9%
기구(gggi 1
 
0.9%
Other values (93) 93
80.2%
2024-04-30T06:49:13.168492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
79
 
9.3%
) 33
 
3.9%
( 33
 
3.9%
I 25
 
2.9%
국 23
 
2.7%
아 21
 
2.5%
C 19
 
2.2%
A 17
 
2.0%
사 17
 
2.0%
무 16
 
1.9%
Other values (170) 568
66.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 448
52.6%
Uppercase Letter 172
 
20.2%
Space Separator 79
 
9.3%
Lowercase Letter 77
 
9.0%
Close Punctuation 33
 
3.9%
Open Punctuation 33
 
3.9%
Dash Punctuation 5
 
0.6%
Other Punctuation 3
 
0.4%
Decimal Number 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
국 23
 
5.1%
아 21
 
4.7%
사 17
 
3.8%
무 16
 
3.6%
소 14
 
3.1%
구 14
 
3.1%
기 12
 
2.7%
한 12
 
2.7%
유 11
 
2.5%
계 10
 
2.2%
Other values (126) 298
66.5%
Uppercase Letter
ValueCountFrequency (%)
I 25
14.5%
C 19
11.0%
A 17
 
9.9%
P 12
 
7.0%
N 10
 
5.8%
F 9
 
5.2%
E 9
 
5.2%
O 9
 
5.2%
U 8
 
4.7%
S 8
 
4.7%
Other values (12) 46
26.7%
Lowercase Letter
ValueCountFrequency (%)
e 13
16.9%
o 13
16.9%
r 10
13.0%
a 9
11.7%
f 5
 
6.5%
i 5
 
6.5%
n 4
 
5.2%
c 3
 
3.9%
l 3
 
3.9%
u 3
 
3.9%
Other values (6) 9
11.7%
Space Separator
ValueCountFrequency (%)
79
100.0%
Close Punctuation
ValueCountFrequency (%)
) 33
100.0%
Open Punctuation
ValueCountFrequency (%)
( 33
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%
Decimal Number
ValueCountFrequency (%)
3 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 448
52.6%
Latin 249
29.3%
Common 154
 
18.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
국 23
 
5.1%
아 21
 
4.7%
사 17
 
3.8%
무 16
 
3.6%
소 14
 
3.1%
구 14
 
3.1%
기 12
 
2.7%
한 12
 
2.7%
유 11
 
2.5%
계 10
 
2.2%
Other values (126) 298
66.5%
Latin
ValueCountFrequency (%)
I 25
 
10.0%
C 19
 
7.6%
A 17
 
6.8%
e 13
 
5.2%
o 13
 
5.2%
P 12
 
4.8%
N 10
 
4.0%
r 10
 
4.0%
F 9
 
3.6%
a 9
 
3.6%
Other values (28) 112
45.0%
Common
ValueCountFrequency (%)
79
51.3%
) 33
21.4%
( 33
21.4%
- 5
 
3.2%
, 3
 
1.9%
3 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 448
52.6%
ASCII 403
47.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
79
19.6%
) 33
 
8.2%
( 33
 
8.2%
I 25
 
6.2%
C 19
 
4.7%
A 17
 
4.2%
e 13
 
3.2%
o 13
 
3.2%
P 12
 
3.0%
N 10
 
2.5%
Other values (34) 149
37.0%
Hangul
ValueCountFrequency (%)
국 23
 
5.1%
아 21
 
4.7%
사 17
 
3.8%
무 16
 
3.6%
소 14
 
3.1%
구 14
 
3.1%
기 12
 
2.7%
한 12
 
2.7%
유 11
 
2.5%
계 10
 
2.2%
Other values (126) 298
66.5%

가입연도
Real number (ℝ)

Distinct18
Distinct (%)48.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010.5676
Minimum1987
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size465.0 B
2024-04-30T06:49:13.291423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1987
5-th percentile1995
Q12009
median2012
Q32016
95-th percentile2019
Maximum2019
Range32
Interquartile range (IQR)7

Descriptive statistics

Standard deviation8.0226781
Coefficient of variation (CV)0.0039902554
Kurtosis2.4404842
Mean2010.5676
Median Absolute Deviation (MAD)3
Skewness-1.6102401
Sum74391
Variance64.363363
MonotonicityDecreasing
2024-04-30T06:49:13.396967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2017 5
13.5%
2015 4
10.8%
2012 4
10.8%
2019 3
 
8.1%
2014 3
 
8.1%
2009 3
 
8.1%
2011 2
 
5.4%
1987 2
 
5.4%
2010 2
 
5.4%
2018 1
 
2.7%
Other values (8) 8
21.6%
ValueCountFrequency (%)
1987 2
5.4%
1997 1
 
2.7%
1999 1
 
2.7%
2000 1
 
2.7%
2001 1
 
2.7%
2005 1
 
2.7%
2007 1
 
2.7%
2009 3
8.1%
2010 2
5.4%
2011 2
5.4%
ValueCountFrequency (%)
2019 3
8.1%
2018 1
 
2.7%
2017 5
13.5%
2016 1
 
2.7%
2015 4
10.8%
2014 3
8.1%
2013 1
 
2.7%
2012 4
10.8%
2011 2
 
5.4%
2010 2
 
5.4%

설립목적
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing37
Missing (%)100.0%
Memory size465.0 B

설립연혁
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing37
Missing (%)100.0%
Memory size465.0 B

회원도시
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing37
Missing (%)100.0%
Memory size465.0 B

회원자격
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing37
Missing (%)100.0%
Memory size465.0 B

회비
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing37
Missing (%)100.0%
Memory size465.0 B

회의주기
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing37
Missing (%)100.0%
Memory size465.0 B

주요활동
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing37
Missing (%)100.0%
Memory size465.0 B

사무국담당자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing37
Missing (%)100.0%
Memory size465.0 B

Interactions

2024-04-30T06:49:11.497244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:49:11.297079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:49:11.571324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T06:49:11.417866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T06:49:13.486536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지대륙명소재지국가명소재지도시명SEQNO기구명가입연도
소재지대륙명1.0001.0001.0000.2311.0000.000
소재지국가명1.0001.0001.0000.0001.0000.550
소재지도시명1.0001.0001.0000.0001.0000.497
SEQNO0.2310.0000.0001.0001.0000.554
기구명1.0001.0001.0001.0001.0001.000
가입연도0.0000.5500.4970.5541.0001.000
2024-04-30T06:49:13.593315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지국가명소재지도시명소재지대륙명
소재지국가명1.0000.9800.857
소재지도시명0.9801.0000.840
소재지대륙명0.8570.8401.000
2024-04-30T06:49:13.675398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SEQNO가입연도소재지대륙명소재지국가명소재지도시명
SEQNO1.0000.3010.0000.0000.000
가입연도0.3011.0000.0310.3300.290
소재지대륙명0.0000.0311.0000.8570.840
소재지국가명0.0000.3300.8571.0000.980
소재지도시명0.0000.2900.8400.9801.000

Missing values

2024-04-30T06:49:11.693870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T06:49:11.853249image/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

소재지대륙명소재지국가명소재지도시명SEQNO기구명가입연도설립목적설립연혁회원도시회원자격회비회의주기주요활동사무국담당자
0북아메리카미국뉴욕60유엔인구기금 한국사무소 (UNPFA)2019<NA><NA><NA><NA><NA><NA><NA><NA>
1유럽이탈리아로마48유엔식량농업기구 협력연락사무소(FAO)2019<NA><NA><NA><NA><NA><NA><NA><NA>
2유럽스위스제네바59세계보건기구(WHO) 아태환경보건센터2019<NA><NA><NA><NA><NA><NA><NA><NA>
3아시아한국서울47아셈노인인권정책센터(AGAC)2018<NA><NA><NA><NA><NA><NA><NA><NA>
4북아메리카미국뉴욕58휴먼라이츠워치 한국지부(HRW Korea)2017<NA><NA><NA><NA><NA><NA><NA><NA>
5북아메리카미국뉴욕45유엔 아동기금 서울사무소(UNICEF Seoul Office)2017<NA><NA><NA><NA><NA><NA><NA><NA>
6아시아인도네시아자카르타55아시아헌법재판소연합 연구사무국(AACC SRD)2017<NA><NA><NA><NA><NA><NA><NA><NA>
7아시아한국서울24서울국제중재센터2017<NA><NA><NA><NA><NA><NA><NA><NA>
8북아메리카미국뉴욕46세계은행그룹(IFC, MIGA)서울연락사무소(IFC, MIGA)2017<NA><NA><NA><NA><NA><NA><NA><NA>
9아시아한국서울44한-중앙아 협력포럼 사무국2016<NA><NA><NA><NA><NA><NA><NA><NA>
소재지대륙명소재지국가명소재지도시명SEQNO기구명가입연도설립목적설립연혁회원도시회원자격회비회의주기주요활동사무국담당자
27아시아한국서울50아시아정당 국제회의(ICAPP)2009<NA><NA><NA><NA><NA><NA><NA><NA>
28아시아한국서울38한-아세안 센터(AKC)2009<NA><NA><NA><NA><NA><NA><NA><NA>
29유럽프랑스파리36경제협력개발기구 대한민국 정책센터(OECD Korea Policy Centre)2007<NA><NA><NA><NA><NA><NA><NA><NA>
30유럽이탈리아로마35유엔 세계식량계획 한국사무소(WFP Korea)2005<NA><NA><NA><NA><NA><NA><NA><NA>
31유럽스위스제네바34유엔난민기구 한국대표부(UNHCR Korea)2001<NA><NA><NA><NA><NA><NA><NA><NA>
32유럽프랑스파리33유네스코 아태 국제 이해 교육원(UNESCO APCEIU)2000<NA><NA><NA><NA><NA><NA><NA><NA>
33유럽독일본52이클레이-지속가능성을 위한 세계지방정부 동아시아 본부(ICLEI)1999<NA><NA><NA><NA><NA><NA><NA><NA>
34아시아한국서울31국제백신연구소 본부(IVI, HQ)1997<NA><NA><NA><NA><NA><NA><NA><NA>
35유럽오스트리아비엔나30유엔공업개발기구 한국투자진흥사무소(UNIDO ITPO Korea)1987<NA><NA><NA><NA><NA><NA><NA><NA>
36유럽스페인바르셀로나54메트로폴리스 국제연수원 본원(MITI)1987<NA><NA><NA><NA><NA><NA><NA><NA>