Overview

Dataset statistics

Number of variables5
Number of observations43
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 KiB
Average record size in memory44.1 B

Variable types

Categorical3
Text1
Numeric1

Dataset

Description한국수출입은행 전대금융 크레딧 라인의 설정된 현황을 제공하는 데이터입니다. 국가, 은행명, 금액 기준으로 설명을 제공합니다.
URLhttps://www.data.go.kr/data/15012032/fileData.do

Alerts

구분 is highly overall correlated with 국가High correlation
국가 is highly overall correlated with 구분High correlation

Reproduction

Analysis started2023-12-12 16:04:48.033418
Analysis finished2023-12-12 16:04:48.568333
Duration0.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Memory size476.0 B
중남미
19 
CIS
14 
중동
8 
아프리카
2 

Length

Max length4
Median length3
Mean length2.8604651
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
중남미 19
44.2%
CIS 14
32.6%
중동 8
18.6%
아프리카 2
 
4.7%

Length

2023-12-13T01:04:48.672923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:04:48.824924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중남미 19
44.2%
cis 14
32.6%
중동 8
18.6%
아프리카 2
 
4.7%

국가
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)34.9%
Missing0
Missing (%)0.0%
Memory size476.0 B
터키
8 
러시아
7 
우즈벡
6 
베트남
4 
인도
3 
Other values (10)
15 

Length

Max length6
Median length3
Mean length2.8837209
Min length2

Unique

Unique6 ?
Unique (%)14.0%

Sample

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

Common Values

ValueCountFrequency (%)
터키 8
18.6%
러시아 7
16.3%
우즈벡 6
14.0%
베트남 4
9.3%
인도 3
 
7.0%
몽골 3
 
7.0%
브라질 2
 
4.7%
필리핀 2
 
4.7%
나이지리아 2
 
4.7%
아제르바이잔 1
 
2.3%
Other values (5) 5
11.6%

Length

2023-12-13T01:04:48.983763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
터키 8
18.6%
러시아 7
16.3%
우즈벡 6
14.0%
베트남 4
9.3%
인도 3
 
7.0%
몽골 3
 
7.0%
브라질 2
 
4.7%
필리핀 2
 
4.7%
나이지리아 2
 
4.7%
아제르바이잔 1
 
2.3%
Other values (5) 5
11.6%

은행명
Text

Distinct34
Distinct (%)79.1%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-12-13T01:04:49.228324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length22
Mean length11.651163
Min length3

Characters and Unicode

Total characters501
Distinct characters59
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

Unique26 ?
Unique (%)60.5%

Sample

1st rowALFA-BANK
2nd rowALFA-BANK
3rd rowPROMSVYAZBANK
4th rowUniCredit Bank
5th rowJSC VTB BANK
ValueCountFrequency (%)
bank 10
 
11.8%
of 4
 
4.7%
asaka 3
 
3.5%
banco 3
 
3.5%
bdo 2
 
2.4%
mongolia 2
 
2.4%
del 2
 
2.4%
icici 2
 
2.4%
unibank 2
 
2.4%
alfa-bank 2
 
2.4%
Other values (45) 53
62.4%
2023-12-13T01:04:49.698119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 44
 
8.8%
42
 
8.4%
B 35
 
7.0%
a 27
 
5.4%
K 25
 
5.0%
n 24
 
4.8%
I 22
 
4.4%
N 21
 
4.2%
i 20
 
4.0%
S 18
 
3.6%
Other values (49) 223
44.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 276
55.1%
Lowercase Letter 162
32.3%
Space Separator 42
 
8.4%
Other Letter 11
 
2.2%
Other Punctuation 6
 
1.2%
Dash Punctuation 2
 
0.4%
Close Punctuation 1
 
0.2%
Open Punctuation 1
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 44
15.9%
B 35
12.7%
K 25
 
9.1%
I 22
 
8.0%
N 21
 
7.6%
S 18
 
6.5%
O 15
 
5.4%
E 11
 
4.0%
T 11
 
4.0%
U 8
 
2.9%
Other values (13) 66
23.9%
Lowercase Letter
ValueCountFrequency (%)
a 27
16.7%
n 24
14.8%
i 20
12.3%
e 14
8.6%
t 10
 
6.2%
o 10
 
6.2%
c 9
 
5.6%
d 9
 
5.6%
r 9
 
5.6%
k 9
 
5.6%
Other values (9) 21
13.0%
Other Letter
ValueCountFrequency (%)
남 1
9.1%
사 1
9.1%
회 1
9.1%
융 1
9.1%
금 1
9.1%
스 1
9.1%
리 1
9.1%
베 1
9.1%
트 1
9.1%
은 1
9.1%
Other Punctuation
ValueCountFrequency (%)
. 5
83.3%
& 1
 
16.7%
Space Separator
ValueCountFrequency (%)
42
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 438
87.4%
Common 52
 
10.4%
Hangul 11
 
2.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 44
 
10.0%
B 35
 
8.0%
a 27
 
6.2%
K 25
 
5.7%
n 24
 
5.5%
I 22
 
5.0%
N 21
 
4.8%
i 20
 
4.6%
S 18
 
4.1%
O 15
 
3.4%
Other values (32) 187
42.7%
Hangul
ValueCountFrequency (%)
남 1
9.1%
사 1
9.1%
회 1
9.1%
융 1
9.1%
금 1
9.1%
스 1
9.1%
리 1
9.1%
베 1
9.1%
트 1
9.1%
은 1
9.1%
Common
ValueCountFrequency (%)
42
80.8%
. 5
 
9.6%
- 2
 
3.8%
) 1
 
1.9%
& 1
 
1.9%
( 1
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 490
97.8%
Hangul 11
 
2.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 44
 
9.0%
42
 
8.6%
B 35
 
7.1%
a 27
 
5.5%
K 25
 
5.1%
n 24
 
4.9%
I 22
 
4.5%
N 21
 
4.3%
i 20
 
4.1%
S 18
 
3.7%
Other values (38) 212
43.3%
Hangul
ValueCountFrequency (%)
남 1
9.1%
사 1
9.1%
회 1
9.1%
융 1
9.1%
금 1
9.1%
스 1
9.1%
리 1
9.1%
베 1
9.1%
트 1
9.1%
은 1
9.1%

금액
Real number (ℝ)

Distinct17
Distinct (%)39.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean175209.3
Minimum4000
Maximum1000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T01:04:49.861025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4000
5-th percentile10000
Q150000
median100000
Q3195000
95-th percentile700000
Maximum1000000
Range996000
Interquartile range (IQR)145000

Descriptive statistics

Standard deviation246839.02
Coefficient of variation (CV)1.4088237
Kurtosis4.914471
Mean175209.3
Median Absolute Deviation (MAD)70000
Skewness2.3216472
Sum7534000
Variance6.0929503 × 1010
MonotonicityNot monotonic
2023-12-13T01:04:50.011727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
100000 10
23.3%
50000 9
20.9%
30000 3
 
7.0%
200000 3
 
7.0%
300000 2
 
4.7%
700000 2
 
4.7%
10000 2
 
4.7%
20000 2
 
4.7%
1000000 2
 
4.7%
5000 1
 
2.3%
Other values (7) 7
16.3%
ValueCountFrequency (%)
4000 1
 
2.3%
5000 1
 
2.3%
10000 2
 
4.7%
20000 2
 
4.7%
25000 1
 
2.3%
30000 3
 
7.0%
50000 9
20.9%
60000 1
 
2.3%
100000 10
23.3%
150000 1
 
2.3%
ValueCountFrequency (%)
1000000 2
 
4.7%
700000 2
 
4.7%
500000 1
 
2.3%
400000 1
 
2.3%
300000 2
 
4.7%
200000 3
 
7.0%
190000 1
 
2.3%
150000 1
 
2.3%
100000 10
23.3%
60000 1
 
2.3%

비고
Categorical

Distinct8
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Memory size476.0 B
현지법인(간접) + 해외사업활성화(간접)
13 
수출기반(간접)
13 
수출기반(간접) + 현지법인(간접) + 해외사업활성화(간접)
7 
수출기반(간접) + 현지법인(간접) + 해외사업활성화(간접)
4 
수출기반(간접) +외국환
3 
Other values (3)
3 

Length

Max length34
Median length33
Mean length20.139535
Min length8

Unique

Unique3 ?
Unique (%)7.0%

Sample

1st row수출기반(간접) +외국환
2nd row현지법인(간접) + 해외사업활성화(간접)
3rd row수출기반(간접) + 현지법인(간접) + 해외사업활성화(간접)
4th row수출기반(간접) + 현지법인(간접) + 해외사업활성화(간접)
5th row현지법인(간접) + 해외사업활성화(간접)

Common Values

ValueCountFrequency (%)
현지법인(간접) + 해외사업활성화(간접) 13
30.2%
수출기반(간접) 13
30.2%
수출기반(간접) + 현지법인(간접) + 해외사업활성화(간접) 7
16.3%
수출기반(간접) + 현지법인(간접) + 해외사업활성화(간접) 4
 
9.3%
수출기반(간접) +외국환 3
 
7.0%
수출신용장 확인 1
 
2.3%
수출기반(간접)+현지법인(간접)+해외사업활성화(간접) 1
 
2.3%
수출기반(간접)+외국환+현지법인(간접)+해외사업활성화(간접) 1
 
2.3%

Length

2023-12-13T01:04:50.147193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:04:50.275679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
35
29.9%
수출기반(간접 27
23.1%
현지법인(간접 24
20.5%
해외사업활성화(간접 24
20.5%
외국환 3
 
2.6%
수출신용장 1
 
0.9%
확인 1
 
0.9%
수출기반(간접)+현지법인(간접)+해외사업활성화(간접 1
 
0.9%
수출기반(간접)+외국환+현지법인(간접)+해외사업활성화(간접 1
 
0.9%

Interactions

2023-12-13T01:04:48.281161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:04:50.394210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분국가은행명금액비고
구분1.0001.0001.0000.0000.717
국가1.0001.0001.0000.6800.763
은행명1.0001.0001.0000.8410.461
금액0.0000.6800.8411.0000.000
비고0.7170.7630.4610.0001.000
2023-12-13T01:04:50.501134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
국가구분비고
국가1.0000.8470.401
구분0.8471.0000.364
비고0.4010.3641.000
2023-12-13T01:04:50.611097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
금액구분국가비고
금액1.0000.0000.3460.000
구분0.0001.0000.8470.364
국가0.3460.8471.0000.401
비고0.0000.3640.4011.000

Missing values

2023-12-13T01:04:48.417779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:04:48.523474image/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

구분국가은행명금액비고
0CIS러시아ALFA-BANK100000수출기반(간접) +외국환
1CIS러시아ALFA-BANK190000현지법인(간접) + 해외사업활성화(간접)
2CIS러시아PROMSVYAZBANK300000수출기반(간접) + 현지법인(간접) + 해외사업활성화(간접)
3CIS러시아UniCredit Bank200000수출기반(간접) + 현지법인(간접) + 해외사업활성화(간접)
4CIS러시아JSC VTB BANK100000현지법인(간접) + 해외사업활성화(간접)
5CIS러시아VEB200000현지법인(간접) + 해외사업활성화(간접)
6CIS러시아SBER BANK700000현지법인(간접) + 해외사업활성화(간접)
7CIS우즈벡NATIONAL BANK OF UZBEKISTAN100000수출기반(간접)
8CIS우즈벡ASAKA60000수출기반(간접)
9CIS우즈벡ASAKA100000수출신용장 확인
구분국가은행명금액비고
33중남미베트남수은 베트남리스금융회사4000현지법인(간접) + 해외사업활성화(간접)
34중남미베트남VietinBank100000수출기반(간접) + 현지법인(간접) + 해외사업활성화(간접)
35중남미필리핀BDO Unibank30000수출기반(간접)
36중남미필리핀BDO Unibank700000현지법인(간접) + 해외사업활성화(간접)
37중남미몽골GOLOMT BANK OF MONGOLIA5000수출기반(간접)
38중남미몽골KHAN BANK20000수출기반(간접)
39중남미몽골TDBM(TRADE & DEVELOPMENT BANK OF MONGOLIA)30000수출기반(간접)
40중남미스리랑카HNB50000수출기반(간접) + 현지법인(간접) + 해외사업활성화(간접)
41아프리카나이지리아Zenith Bank100000수출기반(간접) + 현지법인(간접) + 해외사업활성화(간접)
42아프리카나이지리아Skye Bank50000수출기반(간접)+외국환+현지법인(간접)+해외사업활성화(간접)