Overview

Dataset statistics

Number of variables5
Number of observations139
Missing cells2
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.7 KiB
Average record size in memory41.9 B

Variable types

Numeric1
Text2
DateTime1
Categorical1

Dataset

Description중소벤처기업진흥공단이 글로벌 유관기관들과 MOU를 통해 구축한 협력선 현황 데이터로 국가명, 기관명, 체결일 등의 자료를 제공합니다.
Author중소벤처기업진흥공단
URLhttps://www.data.go.kr/data/15040632/fileData.do

Alerts

체결일 has 2 (1.4%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 02:22:02.807466
Analysis finished2023-12-12 02:22:03.443583
Duration0.64 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct139
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70
Minimum1
Maximum139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T11:22:03.536429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.9
Q135.5
median70
Q3104.5
95-th percentile132.1
Maximum139
Range138
Interquartile range (IQR)69

Descriptive statistics

Standard deviation40.269923
Coefficient of variation (CV)0.57528461
Kurtosis-1.2
Mean70
Median Absolute Deviation (MAD)35
Skewness0
Sum9730
Variance1621.6667
MonotonicityStrictly increasing
2023-12-12T11:22:03.702292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.7%
97 1
 
0.7%
91 1
 
0.7%
92 1
 
0.7%
93 1
 
0.7%
94 1
 
0.7%
95 1
 
0.7%
96 1
 
0.7%
98 1
 
0.7%
89 1
 
0.7%
Other values (129) 129
92.8%
ValueCountFrequency (%)
1 1
0.7%
2 1
0.7%
3 1
0.7%
4 1
0.7%
5 1
0.7%
6 1
0.7%
7 1
0.7%
8 1
0.7%
9 1
0.7%
10 1
0.7%
ValueCountFrequency (%)
139 1
0.7%
138 1
0.7%
137 1
0.7%
136 1
0.7%
135 1
0.7%
134 1
0.7%
133 1
0.7%
132 1
0.7%
131 1
0.7%
130 1
0.7%

국가명
Text

Distinct55
Distinct (%)39.6%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-12T11:22:03.971886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length3
Min length2

Characters and Unicode

Total characters417
Distinct characters92
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

Unique28 ?
Unique (%)20.1%

Sample

1st row오스트리아
2nd row스위스
3rd row스웨덴
4th row독일
5th row프랑스
ValueCountFrequency (%)
미국 12
 
8.6%
중국 11
 
7.9%
프랑스 8
 
5.7%
러시아 8
 
5.7%
베트남 7
 
5.0%
국제기구 7
 
5.0%
인도 5
 
3.6%
독일 5
 
3.6%
태국 5
 
3.6%
인도네시아 5
 
3.6%
Other values (44) 67
47.9%
2023-12-12T11:22:04.373349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
국 42
 
10.1%
스 26
 
6.2%
아 24
 
5.8%
시 14
 
3.4%
미 13
 
3.1%
인 13
 
3.1%
중 11
 
2.6%
트 11
 
2.6%
도 10
 
2.4%
프 9
 
2.2%
Other values (82) 244
58.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 409
98.1%
Uppercase Letter 6
 
1.4%
Other Punctuation 1
 
0.2%
Space Separator 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
국 42
 
10.3%
스 26
 
6.4%
아 24
 
5.9%
시 14
 
3.4%
미 13
 
3.2%
인 13
 
3.2%
중 11
 
2.7%
트 11
 
2.7%
도 10
 
2.4%
프 9
 
2.2%
Other values (77) 236
57.7%
Uppercase Letter
ValueCountFrequency (%)
E 2
33.3%
U 2
33.3%
A 2
33.3%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 409
98.1%
Latin 6
 
1.4%
Common 2
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
국 42
 
10.3%
스 26
 
6.4%
아 24
 
5.9%
시 14
 
3.4%
미 13
 
3.2%
인 13
 
3.2%
중 11
 
2.7%
트 11
 
2.7%
도 10
 
2.4%
프 9
 
2.2%
Other values (77) 236
57.7%
Latin
ValueCountFrequency (%)
E 2
33.3%
U 2
33.3%
A 2
33.3%
Common
ValueCountFrequency (%)
, 1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 409
98.1%
ASCII 8
 
1.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
국 42
 
10.3%
스 26
 
6.4%
아 24
 
5.9%
시 14
 
3.4%
미 13
 
3.2%
인 13
 
3.2%
중 11
 
2.7%
트 11
 
2.7%
도 10
 
2.4%
프 9
 
2.2%
Other values (77) 236
57.7%
ASCII
ValueCountFrequency (%)
E 2
25.0%
U 2
25.0%
A 2
25.0%
, 1
12.5%
1
12.5%

기관명
Text

Distinct134
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-12T11:22:04.691850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length172
Median length60
Mean length35.294964
Min length3

Characters and Unicode

Total characters4906
Distinct characters225
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique129 ?
Unique (%)92.8%

Sample

1st rowAustrian Federal Economic Chamber _AFEC_
2nd rowSwiss Office for Trade Promotion _OSEC_
3rd rowSweden-Korea Trade Council
4th rowDeutsch-Koreanischer Wirtschaftskreis _DKW_
5th rowAssociation pour la Promotion et le Developpement Industriel
ValueCountFrequency (%)
of 49
 
7.6%
and 33
 
5.1%
for 19
 
2.9%
development 15
 
2.3%
small 15
 
2.3%
agency 14
 
2.2%
the 12
 
1.9%
medium 11
 
1.7%
investment 10
 
1.5%
chamber 10
 
1.5%
Other values (317) 460
71.0%
2023-12-12T11:22:05.189985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
515
 
10.5%
e 367
 
7.5%
n 350
 
7.1%
o 287
 
5.8%
a 251
 
5.1%
r 238
 
4.9%
i 238
 
4.9%
t 234
 
4.8%
s 187
 
3.8%
d 130
 
2.6%
Other values (215) 2109
43.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3086
62.9%
Uppercase Letter 698
 
14.2%
Space Separator 515
 
10.5%
Other Letter 465
 
9.5%
Connector Punctuation 108
 
2.2%
Other Punctuation 12
 
0.2%
Dash Punctuation 9
 
0.2%
Decimal Number 5
 
0.1%
Close Punctuation 4
 
0.1%
Open Punctuation 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 22
 
4.7%
국 14
 
3.0%
업 13
 
2.8%
공 12
 
2.6%
회 11
 
2.4%
중 11
 
2.4%
소 11
 
2.4%
상 10
 
2.2%
학 8
 
1.7%
청 8
 
1.7%
Other values (153) 345
74.2%
Lowercase Letter
ValueCountFrequency (%)
e 367
11.9%
n 350
11.3%
o 287
9.3%
a 251
 
8.1%
r 238
 
7.7%
i 238
 
7.7%
t 234
 
7.6%
s 187
 
6.1%
d 130
 
4.2%
m 122
 
4.0%
Other values (15) 682
22.1%
Uppercase Letter
ValueCountFrequency (%)
I 91
13.0%
C 74
10.6%
S 59
 
8.5%
A 57
 
8.2%
E 48
 
6.9%
M 47
 
6.7%
D 38
 
5.4%
T 38
 
5.4%
B 35
 
5.0%
F 32
 
4.6%
Other values (15) 179
25.6%
Decimal Number
ValueCountFrequency (%)
2 2
40.0%
1 1
20.0%
4 1
20.0%
3 1
20.0%
Other Punctuation
ValueCountFrequency (%)
, 6
50.0%
. 4
33.3%
& 2
 
16.7%
Space Separator
ValueCountFrequency (%)
515
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 108
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Close Punctuation
ValueCountFrequency (%)
] 4
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3784
77.1%
Common 657
 
13.4%
Hangul 445
 
9.1%
Han 20
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 22
 
4.9%
국 14
 
3.1%
업 13
 
2.9%
공 12
 
2.7%
회 11
 
2.5%
중 11
 
2.5%
소 11
 
2.5%
상 10
 
2.2%
학 8
 
1.8%
청 8
 
1.8%
Other values (143) 325
73.0%
Latin
ValueCountFrequency (%)
e 367
 
9.7%
n 350
 
9.2%
o 287
 
7.6%
a 251
 
6.6%
r 238
 
6.3%
i 238
 
6.3%
t 234
 
6.2%
s 187
 
4.9%
d 130
 
3.4%
m 122
 
3.2%
Other values (40) 1380
36.5%
Common
ValueCountFrequency (%)
515
78.4%
_ 108
 
16.4%
- 9
 
1.4%
, 6
 
0.9%
] 4
 
0.6%
[ 4
 
0.6%
. 4
 
0.6%
2 2
 
0.3%
& 2
 
0.3%
1 1
 
0.2%
Other values (2) 2
 
0.3%
Han
ValueCountFrequency (%)
小 2
10.0%
企 2
10.0%
中 2
10.0%
構 2
10.0%
備 2
10.0%
整 2
10.0%
盤 2
10.0%
機 2
10.0%
業 2
10.0%
基 2
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4441
90.5%
Hangul 445
 
9.1%
CJK 20
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
515
 
11.6%
e 367
 
8.3%
n 350
 
7.9%
o 287
 
6.5%
a 251
 
5.7%
r 238
 
5.4%
i 238
 
5.4%
t 234
 
5.3%
s 187
 
4.2%
d 130
 
2.9%
Other values (52) 1644
37.0%
Hangul
ValueCountFrequency (%)
기 22
 
4.9%
국 14
 
3.1%
업 13
 
2.9%
공 12
 
2.7%
회 11
 
2.5%
중 11
 
2.5%
소 11
 
2.5%
상 10
 
2.2%
학 8
 
1.8%
청 8
 
1.8%
Other values (143) 325
73.0%
CJK
ValueCountFrequency (%)
小 2
10.0%
企 2
10.0%
中 2
10.0%
構 2
10.0%
備 2
10.0%
整 2
10.0%
盤 2
10.0%
機 2
10.0%
業 2
10.0%
基 2
10.0%

체결일
Date

MISSING 

Distinct128
Distinct (%)93.4%
Missing2
Missing (%)1.4%
Memory size1.2 KiB
Minimum1990-03-28 00:00:00
Maximum2023-06-23 00:00:00
2023-12-12T11:22:05.348594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:22:05.484415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

유효기간
Categorical

Distinct46
Distinct (%)33.1%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
자동갱신
87 
2023-09-23
 
2
2024-03-21
 
2
2025-06-20
 
2
2024-04-23
 
2
Other values (41)
44 

Length

Max length23
Median length4
Mean length6.6402878
Min length4

Unique

Unique38 ?
Unique (%)27.3%

Sample

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

Common Values

ValueCountFrequency (%)
자동갱신 87
62.6%
2023-09-23 2
 
1.4%
2024-03-21 2
 
1.4%
2025-06-20 2
 
1.4%
2024-04-23 2
 
1.4%
2023-11-21 2
 
1.4%
2026-02-26 2
 
1.4%
자동갱신_3년단위_ 2
 
1.4%
2022-06-10_3년마다 자동갱신_ 1
 
0.7%
2023-10-26 1
 
0.7%
Other values (36) 36
25.9%

Length

2023-12-12T11:22:05.609043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
자동갱신 93
63.7%
2024-03-21 2
 
1.4%
2025-06-20 2
 
1.4%
2024-04-23 2
 
1.4%
2023-11-21 2
 
1.4%
2026-02-26 2
 
1.4%
자동갱신_3년단위 2
 
1.4%
2023-09-23 2
 
1.4%
2025-03-09 1
 
0.7%
2024-03-23 1
 
0.7%
Other values (37) 37
 
25.3%

Interactions

2023-12-12T11:22:03.139345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:22:05.683030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번국가명유효기간
연번1.0000.6570.622
국가명0.6571.0000.776
유효기간0.6220.7761.000
2023-12-12T11:22:05.761010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번유효기간
연번1.0000.225
유효기간0.2251.000

Missing values

2023-12-12T11:22:03.268354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:22:03.398183image/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

연번국가명기관명체결일유효기간
01오스트리아Austrian Federal Economic Chamber _AFEC_1990-03-28자동갱신
12스위스Swiss Office for Trade Promotion _OSEC_1991-03-20자동갱신
23스웨덴Sweden-Korea Trade Council1992-02-21자동갱신
34독일Deutsch-Koreanischer Wirtschaftskreis _DKW_1992-04-28자동갱신
45프랑스Association pour la Promotion et le Developpement Industriel1996-04-12자동갱신
56영국RTC North Ltd., Sunderland _RTC_1993-03-17자동갱신
67스위스Swiss-Korean Chamber of Commerce1993-07-30자동갱신
78스리랑카Board of Investment _BOI_1993-09-17자동갱신
89미국American Management Association International1994-01-21자동갱신
910프랑스BPI France_프랑스 공적은행_2018-10-152021-10-14
연번국가명기관명체결일유효기간
129130한국KB금융지주2022-09-212023-09-21
130131멕시코멕시코 상공회의소2022-11-082025-11-07
131132국제기구GGGI_글로벌녹색성장기구_2022-11-08자동갱신
132133칠레칠레 산업진흥청_CORFO_2022-11-102024-11-09
133134인도인도 공과대학_IIT_ FITT2023-02-272026-02-26
134135인도인도 중소기업공사_NSIC_2023-02-272026-02-26
135136사우디키자드_칼라피산업지구_2023-03-032025-03-02
136137사우디Saudi Deputy Minister of Inverstment_사우디투자부_2023-03-092025-03-09
137138한국신한금융지주2023-04-052024-04-05
138139베트남롯데벤처스2023-06-232024-06-23