Overview

Dataset statistics

Number of variables20
Number of observations297
Missing cells1361
Missing cells (%)22.9%
Duplicate rows12
Duplicate rows (%)4.0%
Total size in memory48.6 KiB
Average record size in memory167.4 B

Variable types

Categorical8
Text6
Unsupported3
Numeric3

Dataset

Description대륙명,국가명,도시명,협정구분,분야,체결년도,결연체결일,결연자,결연장소,협정서명,주요내용,협정서,성명(한글),성명(원어),성별,소속,직위,취임일,이임일,교류현황
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-2486/S/1/datasetView.do

Alerts

협정구분 has constant value ""Constant
Dataset has 12 (4.0%) duplicate rowsDuplicates
협정서 has a high cardinality: 51 distinct valuesHigh cardinality
국가명 is highly overall correlated with 체결년도 and 6 other fieldsHigh correlation
이임일 is highly overall correlated with 체결년도 and 7 other fieldsHigh correlation
대륙명 is highly overall correlated with 국가명 and 2 other fieldsHigh correlation
결연장소 is highly overall correlated with 체결년도 and 6 other fieldsHigh correlation
협정서 is highly overall correlated with 체결년도 and 5 other fieldsHigh correlation
성별 is highly overall correlated with 이임일High correlation
결연자 is highly overall correlated with 체결년도 and 5 other fieldsHigh correlation
체결년도 is highly overall correlated with 결연체결일 and 5 other fieldsHigh correlation
결연체결일 is highly overall correlated with 체결년도 and 5 other fieldsHigh correlation
취임일 is highly overall correlated with 결연장소 and 1 other fieldsHigh correlation
이임일 is highly imbalanced (91.9%)Imbalance
분야 has 297 (100.0%) missing valuesMissing
주요내용 has 297 (100.0%) missing valuesMissing
성명(한글) has 59 (19.9%) missing valuesMissing
성명(원어) has 15 (5.1%) missing valuesMissing
소속 has 70 (23.6%) missing valuesMissing
직위 has 56 (18.9%) missing valuesMissing
취임일 has 270 (90.9%) missing valuesMissing
교류현황 has 297 (100.0%) missing valuesMissing
분야 is an unsupported type, check if it needs cleaning or further analysisUnsupported
주요내용 is an unsupported type, check if it needs cleaning or further analysisUnsupported
교류현황 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-21 00:34:18.298712
Analysis finished2024-04-21 00:34:24.521631
Duration6.22 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대륙명
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
아시아
148 
유럽
81 
북아메리카
47 
남아메리카
 
11
아프리카
 
7
Other values (2)
 
3

Length

Max length6
Median length5
Mean length3.1683502
Min length2

Unique

Unique1 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
아시아 148
49.8%
유럽 81
27.3%
북아메리카 47
 
15.8%
남아메리카 11
 
3.7%
아프리카 7
 
2.4%
중앙아메리카 2
 
0.7%
오세아니아 1
 
0.3%

Length

2024-04-21T09:34:24.653092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T09:34:24.834497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
아시아 148
49.8%
유럽 81
27.3%
북아메리카 47
 
15.8%
남아메리카 11
 
3.7%
아프리카 7
 
2.4%
중앙아메리카 2
 
0.7%
오세아니아 1
 
0.3%

국가명
Categorical

HIGH CORRELATION 

Distinct38
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
중국
55 
미국
42 
네팔
33 
우즈베키스탄
18 
스리랑카
16 
Other values (33)
133 

Length

Max length8
Median length2
Mean length2.8821549
Min length2

Unique

Unique8 ?
Unique (%)2.7%

Sample

1st row아일랜드
2nd row미국
3rd row아일랜드
4th row미국
5th row스페인

Common Values

ValueCountFrequency (%)
중국 55
18.5%
미국 42
14.1%
네팔 33
 
11.1%
우즈베키스탄 18
 
6.1%
스리랑카 16
 
5.4%
스페인 12
 
4.0%
헝가리 9
 
3.0%
네덜란드 9
 
3.0%
벨라루스 9
 
3.0%
이란 9
 
3.0%
Other values (28) 85
28.6%

Length

2024-04-21T09:34:25.010484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
중국 55
18.5%
미국 42
14.1%
네팔 33
 
11.1%
우즈베키스탄 18
 
6.1%
스리랑카 16
 
5.4%
스페인 12
 
4.0%
헝가리 9
 
3.0%
네덜란드 9
 
3.0%
벨라루스 9
 
3.0%
이란 9
 
3.0%
Other values (28) 85
28.6%

도시명
Text

Distinct54
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2024-04-21T09:34:25.281914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length3.8350168
Min length2

Characters and Unicode

Total characters1139
Distinct characters113
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

Unique17 ?
Unique (%)5.7%

Sample

1st row더블린
2nd row기타
3rd row더블린
4th row뉴저지
5th row마드리드
ValueCountFrequency (%)
카투만두 33
 
11.1%
로스엔젤레스 24
 
8.1%
타슈켄트 18
 
6.1%
휴스턴 16
 
5.4%
콜롬보 16
 
5.4%
텐진 11
 
3.7%
바르셀로나 10
 
3.4%
민스크 9
 
3.0%
산둥성 9
 
3.0%
부다페스트 9
 
3.0%
Other values (44) 142
47.8%
2024-04-21T09:34:25.764138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 125
 
11.0%
카 45
 
4.0%
트 37
 
3.2%
성 36
 
3.2%
레 35
 
3.1%
로 34
 
3.0%
투 33
 
2.9%
두 33
 
2.9%
만 33
 
2.9%
엔 28
 
2.5%
Other values (103) 700
61.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1139
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 125
 
11.0%
카 45
 
4.0%
트 37
 
3.2%
성 36
 
3.2%
레 35
 
3.1%
로 34
 
3.0%
투 33
 
2.9%
두 33
 
2.9%
만 33
 
2.9%
엔 28
 
2.5%
Other values (103) 700
61.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1139
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 125
 
11.0%
카 45
 
4.0%
트 37
 
3.2%
성 36
 
3.2%
레 35
 
3.1%
로 34
 
3.0%
투 33
 
2.9%
두 33
 
2.9%
만 33
 
2.9%
엔 28
 
2.5%
Other values (103) 700
61.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1139
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 125
 
11.0%
카 45
 
4.0%
트 37
 
3.2%
성 36
 
3.2%
레 35
 
3.1%
로 34
 
3.0%
투 33
 
2.9%
두 33
 
2.9%
만 33
 
2.9%
엔 28
 
2.5%
Other values (103) 700
61.5%

협정구분
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
우호도시
297 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
우호도시 297
100.0%

Length

2024-04-21T09:34:25.963640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T09:34:26.088640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
우호도시 297
100.0%

분야
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing297
Missing (%)100.0%
Memory size2.7 KiB

체결년도
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.0572
Minimum1997
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-04-21T09:34:26.230582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1997
5-th percentile1999
Q12008
median2012
Q32016
95-th percentile2018
Maximum2023
Range26
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.4772339
Coefficient of variation (CV)0.0027235594
Kurtosis0.010662885
Mean2011.0572
Median Absolute Deviation (MAD)4
Skewness-0.511109
Sum597284
Variance30.000091
MonotonicityDecreasing
2024-04-21T09:34:26.385083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2016 53
17.8%
2006 34
11.4%
2014 33
11.1%
2008 33
11.1%
2012 21
 
7.1%
2009 19
 
6.4%
2005 17
 
5.7%
2010 16
 
5.4%
2017 15
 
5.1%
2015 13
 
4.4%
Other values (8) 43
14.5%
ValueCountFrequency (%)
1997 9
 
3.0%
1999 9
 
3.0%
2005 17
5.7%
2006 34
11.4%
2007 4
 
1.3%
2008 33
11.1%
2009 19
6.4%
2010 16
5.4%
2011 2
 
0.7%
2012 21
7.1%
ValueCountFrequency (%)
2023 4
 
1.3%
2022 2
 
0.7%
2019 4
 
1.3%
2018 9
 
3.0%
2017 15
 
5.1%
2016 53
17.8%
2015 13
 
4.4%
2014 33
11.1%
2012 21
 
7.1%
2011 2
 
0.7%

결연체결일
Real number (ℝ)

HIGH CORRELATION 

Distinct55
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20111334
Minimum19970110
Maximum20231018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-04-21T09:34:26.555159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19970110
5-th percentile19990315
Q120080708
median20120625
Q320160608
95-th percentile20180908
Maximum20231018
Range260908
Interquartile range (IQR)79900

Descriptive statistics

Standard deviation54897.307
Coefficient of variation (CV)0.0027296701
Kurtosis0.010603987
Mean20111334
Median Absolute Deviation (MAD)39983
Skewness-0.51553055
Sum5.9730661 × 109
Variance3.0137143 × 109
MonotonicityNot monotonic
2024-04-21T09:34:26.763248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20160926 33
 
11.1%
20060330 18
 
6.1%
20141001 16
 
5.4%
20160608 16
 
5.4%
20061016 16
 
5.4%
20090412 11
 
3.7%
20121113 10
 
3.4%
20171018 9
 
3.0%
20080719 9
 
3.0%
20080708 9
 
3.0%
Other values (45) 150
50.5%
ValueCountFrequency (%)
19970110 2
 
0.7%
19970423 7
 
2.4%
19990315 9
3.0%
20050714 9
3.0%
20050824 8
2.7%
20060330 18
6.1%
20061016 16
5.4%
20070201 4
 
1.3%
20080708 9
3.0%
20080719 9
3.0%
ValueCountFrequency (%)
20231018 1
0.3%
20230316 2
0.7%
20230216 1
0.3%
20221026 1
0.3%
20220929 1
0.3%
20190712 1
0.3%
20190530 1
0.3%
20190506 2
0.7%
20181005 1
0.3%
20181003 1
0.3%

결연자
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
박원순
146 
오세훈
96 
이명박
35 
고건
 
9
조순
 
9

Length

Max length3
Median length3
Mean length2.9393939
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
박원순 146
49.2%
오세훈 96
32.3%
이명박 35
 
11.8%
고건 9
 
3.0%
조순 9
 
3.0%
류경기 2
 
0.7%

Length

2024-04-21T09:34:26.943171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T09:34:27.105053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
박원순 146
49.2%
오세훈 96
32.3%
이명박 35
 
11.8%
고건 9
 
3.0%
조순 9
 
3.0%
류경기 2
 
0.7%

결연장소
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
서울
121 
서울시청
46 
톈진시
 
11
바르셀로나
 
10
암스테르담
 
9
Other values (26)
100 

Length

Max length13
Median length11
Mean length3.8451178
Min length2

Unique

Unique9 ?
Unique (%)3.0%

Sample

1st row더블린 맨션하우스
2nd row서울시청 시장 집무실
3rd row더블린 맨션하우스
4th row대한민국 서울
5th row마드리드 시청사

Common Values

ValueCountFrequency (%)
서울 121
40.7%
서울시청 46
 
15.5%
톈진시 11
 
3.7%
바르셀로나 10
 
3.4%
암스테르담 9
 
3.0%
산둥성 지난시 9
 
3.0%
로스앤젤레스시청 8
 
2.7%
광둥성 광저우시 8
 
2.7%
저장성 항저우시 8
 
2.7%
부에노스아이레스 8
 
2.7%
Other values (21) 59
19.9%

Length

2024-04-21T09:34:27.271148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울 122
35.8%
서울시청 48
 
14.1%
톈진시 11
 
3.2%
바르셀로나 10
 
2.9%
암스테르담 9
 
2.6%
산둥성 9
 
2.6%
지난시 9
 
2.6%
저장성 8
 
2.3%
부에노스아이레스 8
 
2.3%
항저우시 8
 
2.3%
Other values (30) 99
29.0%

협정서명
Text

Distinct58
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2024-04-21T09:34:27.607050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length86
Median length43
Mean length36.690236
Min length6

Characters and Unicode

Total characters10897
Distinct characters198
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)6.7%

Sample

1st row아일랜드 더블린 시와 대한민국 서울특별시간 우호협정서
2nd row우호 및 협력관계에 관한 양해각서
3rd row아일랜드 더블린 시와 대한민국 서울특별시간 우호협정서
4th row대한민국 서울특별시와 미합중국 뉴저지주 간 우호 협력도시 체결을 위한 협약서
5th row서울특별시(대한민국) 및 마드리드시 (스페인왕국) 간 우호협력 결연에 관한 양해각서
ValueCountFrequency (%)
대한민국 215
 
9.5%
서울특별시와 203
 
8.9%
관한 192
 
8.5%
간 156
 
6.9%
양해각서 122
 
5.4%
우호 108
 
4.8%
교류협력에 74
 
3.3%
합의서 74
 
3.3%
서울시와 63
 
2.8%
중화인민공화국 55
 
2.4%
Other values (141) 1007
44.4%
2024-04-21T09:34:28.432602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1972
 
18.1%
서 595
 
5.5%
시 515
 
4.7%
한 437
 
4.0%
국 354
 
3.2%
협 334
 
3.1%
와 332
 
3.0%
울 302
 
2.8%
간 295
 
2.7%
민 283
 
2.6%
Other values (188) 5478
50.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8719
80.0%
Space Separator 1972
 
18.1%
Lowercase Letter 64
 
0.6%
Uppercase Letter 48
 
0.4%
Dash Punctuation 44
 
0.4%
Close Punctuation 21
 
0.2%
Open Punctuation 21
 
0.2%
Other Punctuation 8
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 595
 
6.8%
시 515
 
5.9%
한 437
 
5.0%
국 354
 
4.1%
협 334
 
3.8%
와 332
 
3.8%
울 302
 
3.5%
간 295
 
3.4%
민 283
 
3.2%
력 251
 
2.9%
Other values (176) 5021
57.6%
Lowercase Letter
ValueCountFrequency (%)
e 16
25.0%
i 16
25.0%
v 16
25.0%
r 16
25.0%
Uppercase Letter
ValueCountFrequency (%)
A 16
33.3%
L 16
33.3%
R 16
33.3%
Space Separator
ValueCountFrequency (%)
1972
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Other Punctuation
ValueCountFrequency (%)
, 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8719
80.0%
Common 2066
 
19.0%
Latin 112
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 595
 
6.8%
시 515
 
5.9%
한 437
 
5.0%
국 354
 
4.1%
협 334
 
3.8%
와 332
 
3.8%
울 302
 
3.5%
간 295
 
3.4%
민 283
 
3.2%
력 251
 
2.9%
Other values (176) 5021
57.6%
Latin
ValueCountFrequency (%)
A 16
14.3%
e 16
14.3%
L 16
14.3%
R 16
14.3%
i 16
14.3%
v 16
14.3%
r 16
14.3%
Common
ValueCountFrequency (%)
1972
95.5%
- 44
 
2.1%
) 21
 
1.0%
( 21
 
1.0%
, 8
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8719
80.0%
ASCII 2178
 
20.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1972
90.5%
- 44
 
2.0%
) 21
 
1.0%
( 21
 
1.0%
A 16
 
0.7%
e 16
 
0.7%
L 16
 
0.7%
R 16
 
0.7%
i 16
 
0.7%
v 16
 
0.7%
Other values (2) 24
 
1.1%
Hangul
ValueCountFrequency (%)
서 595
 
6.8%
시 515
 
5.9%
한 437
 
5.0%
국 354
 
4.1%
협 334
 
3.8%
와 332
 
3.8%
울 302
 
3.5%
간 295
 
3.4%
민 283
 
3.2%
력 251
 
2.9%
Other values (176) 5021
57.6%

주요내용
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing297
Missing (%)100.0%
Memory size2.7 KiB

협정서
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct51
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
<NA>
45 
서울시-카트만두 우호도시 협정(160926).pdf
33 
서울 휴스턴 우호도시.pdf
 
16
서울 콜롬보 우호도시.pdf
 
16
서울-중국 천진시간 협의서(한)_2009.4.12.jpg
 
11
Other values (46)
176 

Length

Max length43
Median length34
Mean length20.252525
Min length4

Unique

Unique16 ?
Unique (%)5.4%

Sample

1st row우호도시협정체결 MOU 스캔본.pdf
2nd row<NA>
3rd row우호도시협정체결 MOU 스캔본.pdf
4th row208. 서울-뉴저지주간 우호도시협정 MOU(국영) ('23.10).pdf
5th row마드리드 MOU_En.pdf

Common Values

ValueCountFrequency (%)
<NA> 45
 
15.2%
서울시-카트만두 우호도시 협정(160926).pdf 33
 
11.1%
서울 휴스턴 우호도시.pdf 16
 
5.4%
서울 콜롬보 우호도시.pdf 16
 
5.4%
서울-중국 천진시간 협의서(한)_2009.4.12.jpg 11
 
3.7%
서울-바르셀로나MOU(카탈란).jpg 10
 
3.4%
부다페스트_헝가리_우호도시.pdf 9
 
3.0%
테헤란시 우호도시 협정체결 협약서(171018).pdf 9
 
3.0%
서울시-민스크시 협정서.PDF 9
 
3.0%
서울-중국 산동성간 협의서(한)_2008.7.19.jpg 9
 
3.0%
Other values (41) 130
43.8%

Length

2024-04-21T09:34:28.641775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울 59
 
8.1%
우호도시.pdf 54
 
7.4%
협정서.pdf 52
 
7.1%
우호도시 48
 
6.6%
na 45
 
6.2%
서울-중국 43
 
5.9%
협정(160926).pdf 33
 
4.5%
서울시-카트만두 33
 
4.5%
휴스턴 16
 
2.2%
콜롬보 16
 
2.2%
Other values (86) 331
45.3%

성명(한글)
Text

MISSING 

Distinct212
Distinct (%)89.1%
Missing59
Missing (%)19.9%
Memory size2.4 KiB
2024-04-21T09:34:29.059860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length13
Mean length7.4705882
Min length2

Characters and Unicode

Total characters1778
Distinct characters291
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique200 ?
Unique (%)84.0%

Sample

1st row카디자 알 야하메디
2nd row자키야 알 암리
3rd row야세르 알 바트타시
4th row호세 블란돈
5th row안숙자
ValueCountFrequency (%)
엘가 9
 
1.7%
샤프 9
 
1.7%
안토니오 6
 
1.1%
비야라이고사 6
 
1.1%
리 5
 
0.9%
에릭 4
 
0.8%
알리 3
 
0.6%
반 3
 
0.6%
바하더 3
 
0.6%
알 3
 
0.6%
Other values (430) 481
90.4%
2024-04-21T09:34:29.732693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
295
 
16.6%
리 61
 
3.4%
라 58
 
3.3%
스 46
 
2.6%
이 43
 
2.4%
아 34
 
1.9%
드 32
 
1.8%
마 32
 
1.8%
프 31
 
1.7%
사 26
 
1.5%
Other values (281) 1120
63.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1475
83.0%
Space Separator 295
 
16.6%
Lowercase Letter 7
 
0.4%
Uppercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 61
 
4.1%
라 58
 
3.9%
스 46
 
3.1%
이 43
 
2.9%
아 34
 
2.3%
드 32
 
2.2%
마 32
 
2.2%
프 31
 
2.1%
사 26
 
1.8%
카 25
 
1.7%
Other values (272) 1087
73.7%
Lowercase Letter
ValueCountFrequency (%)
s 1
14.3%
k 1
14.3%
i 1
14.3%
o 1
14.3%
w 1
14.3%
n 1
14.3%
a 1
14.3%
Space Separator
ValueCountFrequency (%)
295
100.0%
Uppercase Letter
ValueCountFrequency (%)
J 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1475
83.0%
Common 295
 
16.6%
Latin 8
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 61
 
4.1%
라 58
 
3.9%
스 46
 
3.1%
이 43
 
2.9%
아 34
 
2.3%
드 32
 
2.2%
마 32
 
2.2%
프 31
 
2.1%
사 26
 
1.8%
카 25
 
1.7%
Other values (272) 1087
73.7%
Latin
ValueCountFrequency (%)
s 1
12.5%
k 1
12.5%
i 1
12.5%
o 1
12.5%
w 1
12.5%
n 1
12.5%
a 1
12.5%
J 1
12.5%
Common
ValueCountFrequency (%)
295
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1475
83.0%
ASCII 303
 
17.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
295
97.4%
s 1
 
0.3%
k 1
 
0.3%
i 1
 
0.3%
o 1
 
0.3%
w 1
 
0.3%
n 1
 
0.3%
a 1
 
0.3%
J 1
 
0.3%
Hangul
ValueCountFrequency (%)
리 61
 
4.1%
라 58
 
3.9%
스 46
 
3.1%
이 43
 
2.9%
아 34
 
2.3%
드 32
 
2.2%
마 32
 
2.2%
프 31
 
2.1%
사 26
 
1.8%
카 25
 
1.7%
Other values (272) 1087
73.7%

성명(원어)
Text

MISSING 

Distinct248
Distinct (%)87.9%
Missing15
Missing (%)5.1%
Memory size2.4 KiB
2024-04-21T09:34:30.066353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length35
Mean length16.964539
Min length3

Characters and Unicode

Total characters4784
Distinct characters100
Distinct categories9 ?
Distinct scripts5 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique232 ?
Unique (%)82.3%

Sample

1st rowNaoise &Oacute; Muir&iacute;
2nd rowNaoise &Oacute; Muir&iacute;
3rd rowAna Botella
4th rowGuy Morin
5th rowPeer Visner
ValueCountFrequency (%)
elga 9
 
1.3%
sharpe 9
 
1.3%
li 7
 
1.0%
de 6
 
0.9%
antonio 6
 
0.9%
villaraigosa 6
 
0.9%
bahadur 5
 
0.7%
michael 5
 
0.7%
eric 5
 
0.7%
adhikari 4
 
0.6%
Other values (519) 630
91.0%
2024-04-21T09:34:30.643986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 565
 
11.8%
435
 
9.1%
i 319
 
6.7%
n 271
 
5.7%
e 266
 
5.6%
r 245
 
5.1%
o 196
 
4.1%
h 181
 
3.8%
l 147
 
3.1%
u 144
 
3.0%
Other values (90) 2015
42.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3230
67.5%
Uppercase Letter 928
 
19.4%
Space Separator 438
 
9.2%
Other Punctuation 84
 
1.8%
Decimal Number 47
 
1.0%
Other Letter 31
 
0.6%
Open Punctuation 10
 
0.2%
Close Punctuation 10
 
0.2%
Dash Punctuation 6
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 565
17.5%
i 319
9.9%
n 271
 
8.4%
e 266
 
8.2%
r 245
 
7.6%
o 196
 
6.1%
h 181
 
5.6%
l 147
 
4.6%
u 144
 
4.5%
t 125
 
3.9%
Other values (16) 771
23.9%
Uppercase Letter
ValueCountFrequency (%)
A 92
 
9.9%
M 75
 
8.1%
S 66
 
7.1%
R 52
 
5.6%
E 50
 
5.4%
H 46
 
5.0%
K 46
 
5.0%
B 45
 
4.8%
J 40
 
4.3%
L 39
 
4.2%
Other values (16) 377
40.6%
Other Letter
ValueCountFrequency (%)
高 2
 
6.5%
橋 2
 
6.5%
る 2
 
6.5%
み 2
 
6.5%
正 2
 
6.5%
は 2
 
6.5%
家 1
 
3.2%
瑞 1
 
3.2%
政 1
 
3.2%
隆 1
 
3.2%
Other values (15) 15
48.4%
Decimal Number
ValueCountFrequency (%)
2 13
27.7%
5 7
14.9%
8 6
12.8%
3 4
 
8.5%
9 3
 
6.4%
1 3
 
6.4%
4 3
 
6.4%
0 3
 
6.4%
6 3
 
6.4%
7 2
 
4.3%
Other Punctuation
ValueCountFrequency (%)
. 21
25.0%
; 21
25.0%
& 21
25.0%
# 10
11.9%
? 6
 
7.1%
, 4
 
4.8%
？ 1
 
1.2%
Space Separator
ValueCountFrequency (%)
435
99.3%
　 3
 
0.7%
Close Punctuation
ValueCountFrequency (%)
) 9
90.0%
） 1
 
10.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4158
86.9%
Common 595
 
12.4%
Han 22
 
0.5%
Hiragana 6
 
0.1%
Hangul 3
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 565
 
13.6%
i 319
 
7.7%
n 271
 
6.5%
e 266
 
6.4%
r 245
 
5.9%
o 196
 
4.7%
h 181
 
4.4%
l 147
 
3.5%
u 144
 
3.5%
t 125
 
3.0%
Other values (42) 1699
40.9%
Common
ValueCountFrequency (%)
435
73.1%
. 21
 
3.5%
; 21
 
3.5%
& 21
 
3.5%
2 13
 
2.2%
( 10
 
1.7%
# 10
 
1.7%
) 9
 
1.5%
5 7
 
1.2%
8 6
 
1.0%
Other values (13) 42
 
7.1%
Han
ValueCountFrequency (%)
高 2
 
9.1%
橋 2
 
9.1%
正 2
 
9.1%
家 1
 
4.5%
瑞 1
 
4.5%
政 1
 
4.5%
隆 1
 
4.5%
袁 1
 
4.5%
行 1
 
4.5%
篠 1
 
4.5%
Other values (9) 9
40.9%
Hiragana
ValueCountFrequency (%)
る 2
33.3%
み 2
33.3%
は 2
33.3%
Hangul
ValueCountFrequency (%)
자 1
33.3%
숙 1
33.3%
안 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4748
99.2%
CJK 22
 
0.5%
Hiragana 6
 
0.1%
None 5
 
0.1%
Hangul 3
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 565
 
11.9%
435
 
9.2%
i 319
 
6.7%
n 271
 
5.7%
e 266
 
5.6%
r 245
 
5.2%
o 196
 
4.1%
h 181
 
3.8%
l 147
 
3.1%
u 144
 
3.0%
Other values (62) 1979
41.7%
None
ValueCountFrequency (%)
　 3
60.0%
？ 1
 
20.0%
） 1
 
20.0%
CJK
ValueCountFrequency (%)
高 2
 
9.1%
橋 2
 
9.1%
正 2
 
9.1%
家 1
 
4.5%
瑞 1
 
4.5%
政 1
 
4.5%
隆 1
 
4.5%
袁 1
 
4.5%
行 1
 
4.5%
篠 1
 
4.5%
Other values (9) 9
40.9%
Hiragana
ValueCountFrequency (%)
る 2
33.3%
み 2
33.3%
は 2
33.3%
Hangul
ValueCountFrequency (%)
자 1
33.3%
숙 1
33.3%
안 1
33.3%

성별
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
M
203 
F
65 
<NA>
29 

Length

Max length4
Median length1
Mean length1.2929293
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd row<NA>
3rd rowM
4th row<NA>
5th rowF

Common Values

ValueCountFrequency (%)
M 203
68.4%
F 65
 
21.9%
<NA> 29
 
9.8%

Length

2024-04-21T09:34:30.831429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T09:34:30.985427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 203
68.4%
f 65
 
21.9%
na 29
 
9.8%

소속
Text

MISSING 

Distinct172
Distinct (%)75.8%
Missing70
Missing (%)23.6%
Memory size2.4 KiB
2024-04-21T09:34:31.320899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length76
Median length56
Mean length21.070485
Min length2

Characters and Unicode

Total characters4783
Distinct characters235
Distinct categories11 ?
Distinct scripts5 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique149 ?
Unique (%)65.6%

Sample

1st rowInformation Technology Department/ System Development
2nd rowInformation Technology Department/ System Development
3rd rowInformation Systems DG
4th row파나마시티
5th rowTehran Traffic and Transportation Organization
ValueCountFrequency (%)
department 24
 
3.8%
of 22
 
3.5%
city 22
 
3.5%
metropolitan 20
 
3.2%
kathmandu 16
 
2.5%
and 14
 
2.2%
office 14
 
2.2%
municipal 13
 
2.1%
state 12
 
1.9%
로스앤젤레스시 12
 
1.9%
Other values (249) 464
73.3%
2024-04-21T09:34:31.957710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
425
 
8.9%
t 357
 
7.5%
n 337
 
7.0%
a 310
 
6.5%
e 299
 
6.3%
i 298
 
6.2%
o 290
 
6.1%
r 202
 
4.2%
m 134
 
2.8%
l 123
 
2.6%
Other values (225) 2008
42.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3071
64.2%
Other Letter 709
 
14.8%
Uppercase Letter 521
 
10.9%
Space Separator 425
 
8.9%
Other Punctuation 39
 
0.8%
Dash Punctuation 4
 
0.1%
Final Punctuation 4
 
0.1%
Initial Punctuation 3
 
0.1%
Decimal Number 3
 
0.1%
Open Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 68
 
9.6%
시 57
 
8.0%
레 27
 
3.8%
로 22
 
3.1%
실 21
 
3.0%
앤 18
 
2.5%
젤 18
 
2.5%
성 17
 
2.4%
부 17
 
2.4%
국 17
 
2.4%
Other values (161) 427
60.2%
Lowercase Letter
ValueCountFrequency (%)
t 357
11.6%
n 337
11.0%
a 310
10.1%
e 299
9.7%
i 298
9.7%
o 290
9.4%
r 202
 
6.6%
m 134
 
4.4%
l 123
 
4.0%
p 91
 
3.0%
Other values (15) 630
20.5%
Uppercase Letter
ValueCountFrequency (%)
C 90
17.3%
M 64
12.3%
D 46
 
8.8%
T 38
 
7.3%
I 37
 
7.1%
S 36
 
6.9%
O 27
 
5.2%
A 27
 
5.2%
F 19
 
3.6%
K 18
 
3.5%
Other values (13) 119
22.8%
Other Punctuation
ValueCountFrequency (%)
, 10
25.6%
? 8
20.5%
& 8
20.5%
. 5
12.8%
' 5
12.8%
/ 3
 
7.7%
Decimal Number
ValueCountFrequency (%)
1 1
33.3%
4 1
33.3%
3 1
33.3%
Final Punctuation
ValueCountFrequency (%)
” 3
75.0%
’ 1
 
25.0%
Space Separator
ValueCountFrequency (%)
425
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Initial Punctuation
ValueCountFrequency (%)
“ 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3592
75.1%
Hangul 668
 
14.0%
Common 482
 
10.1%
Han 35
 
0.7%
Katakana 6
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 68
 
10.2%
시 57
 
8.5%
레 27
 
4.0%
로 22
 
3.3%
실 21
 
3.1%
앤 18
 
2.7%
젤 18
 
2.7%
성 17
 
2.5%
부 17
 
2.5%
국 17
 
2.5%
Other values (141) 386
57.8%
Latin
ValueCountFrequency (%)
t 357
 
9.9%
n 337
 
9.4%
a 310
 
8.6%
e 299
 
8.3%
i 298
 
8.3%
o 290
 
8.1%
r 202
 
5.6%
m 134
 
3.7%
l 123
 
3.4%
p 91
 
2.5%
Other values (38) 1151
32.0%
Han
ValueCountFrequency (%)
際 4
 
11.4%
海 2
 
5.7%
北 2
 
5.7%
道 2
 
5.7%
政 2
 
5.7%
主 2
 
5.7%
交 2
 
5.7%
課 2
 
5.7%
室 2
 
5.7%
合 2
 
5.7%
Other values (7) 13
37.1%
Common
ValueCountFrequency (%)
425
88.2%
, 10
 
2.1%
? 8
 
1.7%
& 8
 
1.7%
. 5
 
1.0%
' 5
 
1.0%
- 4
 
0.8%
” 3
 
0.6%
“ 3
 
0.6%
/ 3
 
0.6%
Other values (6) 8
 
1.7%
Katakana
ValueCountFrequency (%)
プ 2
33.3%
ル 2
33.3%
グ 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4067
85.0%
Hangul 668
 
14.0%
CJK 35
 
0.7%
Punctuation 7
 
0.1%
Katakana 6
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
425
 
10.4%
t 357
 
8.8%
n 337
 
8.3%
a 310
 
7.6%
e 299
 
7.4%
i 298
 
7.3%
o 290
 
7.1%
r 202
 
5.0%
m 134
 
3.3%
l 123
 
3.0%
Other values (51) 1292
31.8%
Hangul
ValueCountFrequency (%)
스 68
 
10.2%
시 57
 
8.5%
레 27
 
4.0%
로 22
 
3.3%
실 21
 
3.1%
앤 18
 
2.7%
젤 18
 
2.7%
성 17
 
2.5%
부 17
 
2.5%
국 17
 
2.5%
Other values (141) 386
57.8%
CJK
ValueCountFrequency (%)
際 4
 
11.4%
海 2
 
5.7%
北 2
 
5.7%
道 2
 
5.7%
政 2
 
5.7%
主 2
 
5.7%
交 2
 
5.7%
課 2
 
5.7%
室 2
 
5.7%
合 2
 
5.7%
Other values (7) 13
37.1%
Punctuation
ValueCountFrequency (%)
” 3
42.9%
“ 3
42.9%
’ 1
 
14.3%
Katakana
ValueCountFrequency (%)
プ 2
33.3%
ル 2
33.3%
グ 2
33.3%

직위
Text

MISSING 

Distinct133
Distinct (%)55.2%
Missing56
Missing (%)18.9%
Memory size2.4 KiB
2024-04-21T09:34:32.620770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length100
Median length79
Mean length12.854772
Min length2

Characters and Unicode

Total characters3098
Distinct characters116
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique107 ?
Unique (%)44.4%

Sample

1st rowDeputy Mayor
2nd rowSenior IT Developer
3rd rowDeputy Director General
4th rowSenior System Programmer
5th rowSystem Programmer
ValueCountFrequency (%)
시장 39
 
7.7%
chief 22
 
4.4%
officer 21
 
4.2%
of 20
 
4.0%
director 18
 
3.6%
deputy 14
 
2.8%
담당 11
 
2.2%
division 10
 
2.0%
senior 10
 
2.0%
의전실장 10
 
2.0%
Other values (162) 330
65.3%
2024-04-21T09:34:33.314687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
283
 
9.1%
e 274
 
8.8%
i 230
 
7.4%
r 194
 
6.3%
t 187
 
6.0%
n 184
 
5.9%
a 160
 
5.2%
o 153
 
4.9%
f 112
 
3.6%
c 98
 
3.2%
Other values (106) 1223
39.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2082
67.2%
Uppercase Letter 359
 
11.6%
Other Letter 327
 
10.6%
Space Separator 283
 
9.1%
Other Punctuation 28
 
0.9%
Close Punctuation 8
 
0.3%
Open Punctuation 8
 
0.3%
Decimal Number 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 87
26.6%
시 50
15.3%
담 12
 
3.7%
당 11
 
3.4%
의 11
 
3.4%
전 11
 
3.4%
실 11
 
3.4%
국 11
 
3.4%
대 10
 
3.1%
성 9
 
2.8%
Other values (51) 104
31.8%
Lowercase Letter
ValueCountFrequency (%)
e 274
13.2%
i 230
11.0%
r 194
9.3%
t 187
9.0%
n 184
8.8%
a 160
 
7.7%
o 153
 
7.3%
f 112
 
5.4%
c 98
 
4.7%
s 73
 
3.5%
Other values (15) 417
20.0%
Uppercase Letter
ValueCountFrequency (%)
C 54
15.0%
D 50
13.9%
S 47
13.1%
M 31
8.6%
E 30
8.4%
O 29
8.1%
I 18
 
5.0%
P 17
 
4.7%
T 15
 
4.2%
A 15
 
4.2%
Other values (11) 53
14.8%
Other Punctuation
ValueCountFrequency (%)
, 16
57.1%
. 5
 
17.9%
? 4
 
14.3%
& 2
 
7.1%
/ 1
 
3.6%
Space Separator
ValueCountFrequency (%)
283
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Decimal Number
ValueCountFrequency (%)
1 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2441
78.8%
Common 330
 
10.7%
Hangul 327
 
10.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 87
26.6%
시 50
15.3%
담 12
 
3.7%
당 11
 
3.4%
의 11
 
3.4%
전 11
 
3.4%
실 11
 
3.4%
국 11
 
3.4%
대 10
 
3.1%
성 9
 
2.8%
Other values (51) 104
31.8%
Latin
ValueCountFrequency (%)
e 274
 
11.2%
i 230
 
9.4%
r 194
 
7.9%
t 187
 
7.7%
n 184
 
7.5%
a 160
 
6.6%
o 153
 
6.3%
f 112
 
4.6%
c 98
 
4.0%
s 73
 
3.0%
Other values (36) 776
31.8%
Common
ValueCountFrequency (%)
283
85.8%
, 16
 
4.8%
) 8
 
2.4%
( 8
 
2.4%
. 5
 
1.5%
? 4
 
1.2%
1 3
 
0.9%
& 2
 
0.6%
/ 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2771
89.4%
Hangul 327
 
10.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
283
 
10.2%
e 274
 
9.9%
i 230
 
8.3%
r 194
 
7.0%
t 187
 
6.7%
n 184
 
6.6%
a 160
 
5.8%
o 153
 
5.5%
f 112
 
4.0%
c 98
 
3.5%
Other values (45) 896
32.3%
Hangul
ValueCountFrequency (%)
장 87
26.6%
시 50
15.3%
담 12
 
3.7%
당 11
 
3.4%
의 11
 
3.4%
전 11
 
3.4%
실 11
 
3.4%
국 11
 
3.4%
대 10
 
3.1%
성 9
 
2.8%
Other values (51) 104
31.8%

취임일
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)77.8%
Missing270
Missing (%)90.9%
Infinite0
Infinite (%)0.0%
Mean20110764
Minimum20010616
Maximum20171204
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-04-21T09:34:33.584327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20010616
5-th percentile20030423
Q120065866
median20130601
Q320155656
95-th percentile20171060
Maximum20171204
Range160588
Interquartile range (IQR)89790.5

Descriptive statistics

Standard deviation50951.518
Coefficient of variation (CV)0.0025335446
Kurtosis-1.003472
Mean20110764
Median Absolute Deviation (MAD)30425
Skewness-0.50263582
Sum5.4299062 × 108
Variance2.5960571 × 109
MonotonicityNot monotonic
2024-04-21T09:34:33.795618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
20130601 3
 
1.0%
20050701 3
 
1.0%
20110601 2
 
0.7%
20030423 2
 
0.7%
20101003 1
 
0.3%
20101201 1
 
0.3%
20141211 1
 
0.3%
20010616 1
 
0.3%
20100707 1
 
0.3%
20171204 1
 
0.3%
Other values (11) 11
 
3.7%
(Missing) 270
90.9%
ValueCountFrequency (%)
20010616 1
 
0.3%
20030423 2
0.7%
20040328 1
 
0.3%
20050701 3
1.0%
20081030 1
 
0.3%
20100707 1
 
0.3%
20101003 1
 
0.3%
20101201 1
 
0.3%
20110601 2
0.7%
20130601 3
1.0%
ValueCountFrequency (%)
20171204 1
0.3%
20171116 1
0.3%
20170928 1
0.3%
20170827 1
0.3%
20170531 1
0.3%
20161026 1
0.3%
20160102 1
0.3%
20151210 1
0.3%
20141211 1
0.3%
20130916 1
0.3%

이임일
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
<NA>
294 
20130601
 
3

Length

Max length8
Median length4
Mean length4.040404
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 294
99.0%
20130601 3
 
1.0%

Length

2024-04-21T09:34:33.959157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T09:34:34.074560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 294
99.0%
20130601 3
 
1.0%

교류현황
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing297
Missing (%)100.0%
Memory size2.7 KiB

Interactions

2024-04-21T09:34:22.655808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:34:21.833102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:34:22.218903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:34:22.802372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:34:21.988475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:34:22.356204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:34:22.985557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:34:22.087345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T09:34:22.495730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T09:34:34.156719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대륙명국가명도시명체결년도결연체결일결연자결연장소협정서명협정서성별취임일
대륙명1.0001.0000.9990.5530.5510.3130.7841.0000.9990.0660.598
국가명1.0001.0001.0000.9750.9740.9910.9791.0001.0000.2660.838
도시명0.9991.0001.0000.9990.9971.0000.9991.0001.0000.3660.924
체결년도0.5530.9750.9991.0000.9990.8760.9651.0001.0000.1670.650
결연체결일0.5510.9740.9970.9991.0000.8440.9661.0000.9990.0790.218
결연자0.3130.9911.0000.8760.8441.0000.9671.0001.0000.2690.503
결연장소0.7840.9790.9990.9650.9660.9671.0001.0001.0000.0000.746
협정서명1.0001.0001.0001.0001.0001.0001.0001.0001.0000.3480.782
협정서0.9991.0001.0001.0000.9991.0001.0001.0001.0000.2640.827
성별0.0660.2660.3660.1670.0790.2690.0000.3480.2641.0000.611
취임일0.5980.8380.9240.6500.2180.5030.7460.7820.8270.6111.000
2024-04-21T09:34:34.300909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
국가명이임일대륙명결연장소협정서성별결연자
국가명1.0001.0000.9450.6710.9520.1970.886
이임일1.0001.0001.0001.000NaN1.0001.000
대륙명0.9451.0001.0000.4510.8990.0690.192
결연장소0.6711.0000.4511.0000.9500.0000.806
협정서0.952NaN0.8990.9501.0000.1880.905
성별0.1971.0000.0690.0000.1881.0000.192
결연자0.8861.0000.1920.8060.9050.1921.000
2024-04-21T09:34:34.435928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체결년도결연체결일취임일대륙명국가명결연자결연장소협정서성별이임일
체결년도1.0000.9960.3570.3420.8000.6940.7770.9030.1221.000
결연체결일0.9961.0000.3500.3270.7920.6740.7720.9040.0611.000
취임일0.3570.3501.0000.3130.4030.3330.5140.1720.3551.000
대륙명0.3420.3270.3131.0000.9450.1920.4510.8990.0691.000
국가명0.8000.7920.4030.9451.0000.8860.6710.9520.1971.000
결연자0.6940.6740.3330.1920.8861.0000.8060.9050.1921.000
결연장소0.7770.7720.5140.4510.6710.8061.0000.9500.0001.000
협정서0.9030.9040.1720.8990.9520.9050.9501.0000.1880.000
성별0.1220.0610.3550.0690.1970.1920.0000.1881.0001.000
이임일1.0001.0001.0001.0001.0001.0001.0000.0001.0001.000

Missing values

2024-04-21T09:34:23.394177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T09:34:23.920624image/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.
2024-04-21T09:34:24.359932image/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

대륙명국가명도시명협정구분분야체결년도결연체결일결연자결연장소협정서명주요내용협정서성명(한글)성명(원어)성별소속직위취임일이임일교류현황
0유럽아일랜드더블린우호도시<NA>202320230316오세훈더블린 맨션하우스아일랜드 더블린 시와 대한민국 서울특별시간 우호협정서<NA>우호도시협정체결 MOU 스캔본.pdf<NA>Naoise &Oacute; Muir&iacute;M<NA><NA><NA><NA><NA>
1북아메리카미국기타우호도시<NA>202320230216오세훈서울시청 시장 집무실우호 및 협력관계에 관한 양해각서<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
2유럽아일랜드더블린우호도시<NA>202320230316오세훈더블린 맨션하우스아일랜드 더블린 시와 대한민국 서울특별시간 우호협정서<NA>우호도시협정체결 MOU 스캔본.pdf<NA>Naoise &Oacute; Muir&iacute;M<NA><NA><NA><NA><NA>
3북아메리카미국뉴저지우호도시<NA>202320231018오세훈대한민국 서울대한민국 서울특별시와 미합중국 뉴저지주 간 우호 협력도시 체결을 위한 협약서<NA>208. 서울-뉴저지주간 우호도시협정 MOU(국영) ('23.10).pdf<NA><NA><NA><NA><NA><NA><NA><NA>
4유럽스페인마드리드우호도시<NA>202220221026오세훈마드리드 시청사서울특별시(대한민국) 및 마드리드시 (스페인왕국) 간 우호협력 결연에 관한 양해각서<NA>마드리드 MOU_En.pdf<NA>Ana BotellaF<NA><NA><NA><NA><NA>
5유럽스위스바젤우호도시<NA>202220220929오세훈서울시청 8층 간담회장1대한민국 서울특별시와 스위스 바젤슈타트주 간 우호 및 협력관계에 관한 양해각서<NA>180. 서울-바젤슈타트 우호협력도시 합의서.pdf<NA>Guy MorinM<NA><NA><NA><NA><NA>
6남아메리카콜롬비아메데진우호도시<NA>201920190712박원순메데진우호협력 결연에 대한 양해각서<NA>Seoul_Medellin MOU_(19.7.12).pdf<NA><NA><NA><NA><NA><NA><NA><NA>
7아시아이스라엘텔아비브우호도시<NA>201920190506박원순텔아비브대한민국 서울시와 이스라엘 텔아비브시 간 우호 교류 협력을 위한 양해각서<NA>20190506_서울시 텔아비브 우호도시 MOU 협약문.pdf<NA>Peer Visner<NA><NA>Deputy Mayor<NA><NA><NA>
8아시아이스라엘텔아비브우호도시<NA>201920190506박원순텔아비브대한민국 서울시와 이스라엘 텔아비브시 간 우호 교류 협력을 위한 양해각서<NA>20190506_서울시 텔아비브 우호도시 MOU 협약문.pdf<NA>Ron HuldaiM<NA><NA><NA><NA><NA>
9아시아중국충칭우호도시<NA>201920190530박원순서울시대한민국 서울특별시와 중화인민공화국 충칭시 간 우호교류와 협력에 관한 협의서<NA>서울-충칭 우호도시협의서 체결본(2019.5.30).pdf<NA><NA><NA><NA><NA><NA><NA><NA>
대륙명국가명도시명협정구분분야체결년도결연체결일결연자결연장소협정서명주요내용협정서성명(한글)성명(원어)성별소속직위취임일이임일교류현황
287유럽네덜란드암스테르담우호도시<NA>199919990315고건암스테르담서울특별시와 암스테르담시간의 교류와 협력에 관한 합의각서<NA>서울시-암스테르담시 협정서.PDF에베르할드 반 데르 랜Eberhard Edzard van der LaanMMayor of Amsterdam시장20100707<NA><NA>
288유럽독일베를린우호도시<NA>199719970423조순베를린서울특별시-베를린시간 사업협력 및 교류에 관한 각서<NA>서울시-베를린시 협정서.PDF<NA>Rolf Sch?te<NA>베른시 국제협력과의전과장<NA><NA><NA>
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