Overview

Dataset statistics

Number of variables11
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory93.9 KiB
Average record size in memory96.1 B

Variable types

Categorical7
Text1
Numeric3

Dataset

Description신용보증기금의 보증 이용 기업 별로 부여된 보증심사등급 및 신용평가등급에 따른 신용보증 이용 현황에 관한 데이터이니 참고하시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15089413/fileData.do

Alerts

BAS_DT has constant value ""Constant
부점코드 has constant value ""Constant
DM최종수정일자 has constant value ""Constant
취급금액 is highly overall correlated with 보증잔액기업수 and 1 other fieldsHigh correlation
보증잔액기업수 is highly overall correlated with 취급금액 and 1 other fieldsHigh correlation
보증잔액 is highly overall correlated with 취급금액 and 1 other fieldsHigh correlation
통계기업규모코드 is highly imbalanced (95.5%)Imbalance
취급금액 has 516 (51.6%) zerosZeros
보증잔액기업수 has 484 (48.4%) zerosZeros
보증잔액 has 484 (48.4%) zerosZeros

Reproduction

Analysis started2023-12-12 10:47:21.779324
Analysis finished2023-12-12 10:47:24.053688
Duration2.27 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

BAS_DT
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2021-09-15 0:00
1000 

Length

Max length15
Median length15
Mean length15
Min length15

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-09-15 0:00
2nd row2021-09-15 0:00
3rd row2021-09-15 0:00
4th row2021-09-15 0:00
5th row2021-09-15 0:00

Common Values

ValueCountFrequency (%)
2021-09-15 0:00 1000
100.0%

Length

2023-12-12T19:47:24.145682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:47:24.265896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-09-15 1000
50.0%
0:00 1000
50.0%

부점코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
0
1000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1000
100.0%

Length

2023-12-12T19:47:24.407428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:47:24.541537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1000
100.0%

통계기업규모코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2
995 
3
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 995
99.5%
3 5
 
0.5%

Length

2023-12-12T19:47:24.676186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:47:24.798573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 995
99.5%
3 5
 
0.5%

신규보증취급구분코드
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
21
516 
17
258 
13
226 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row21
2nd row17
3rd row17
4th row13
5th row21

Common Values

ValueCountFrequency (%)
21 516
51.6%
17 258
25.8%
13 226
22.6%

Length

2023-12-12T19:47:24.924532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:47:25.076078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
21 516
51.6%
17 258
25.8%
13 226
22.6%

통계신용등급구분코드
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
509 
2
491 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 509
50.9%
2 491
49.1%

Length

2023-12-12T19:47:25.218750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:47:25.372733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 509
50.9%
2 491
49.1%

브릿지론구분코드
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
536 
0
464 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 536
53.6%
0 464
46.4%

Length

2023-12-12T19:47:25.496183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:47:25.632965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 536
53.6%
0 464
46.4%

신용등급값
Text

Distinct65
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2023-12-12T19:47:25.890798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.953
Min length1

Characters and Unicode

Total characters2953
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st rowSB7
2nd rowKS5
3rd rowKS3
4th rowKR11
5th rowKS6
ValueCountFrequency (%)
ks10 50
 
5.0%
ks8 49
 
4.9%
ks9 46
 
4.6%
ks6 44
 
4.4%
ks11 43
 
4.3%
w 40
 
4.0%
ks7 36
 
3.6%
ks5 35
 
3.5%
ks12 34
 
3.4%
k10 31
 
3.1%
Other values (54) 592
59.2%
2023-12-12T19:47:26.391079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
K 795
26.9%
S 567
19.2%
1 410
13.9%
R 142
 
4.8%
B 126
 
4.3%
3 106
 
3.6%
2 99
 
3.4%
4 97
 
3.3%
5 97
 
3.3%
6 97
 
3.3%
Other values (8) 417
14.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1679
56.9%
Decimal Number 1262
42.7%
Math Symbol 8
 
0.3%
Dash Punctuation 4
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 410
32.5%
3 106
 
8.4%
2 99
 
7.8%
4 97
 
7.7%
5 97
 
7.7%
6 97
 
7.7%
9 96
 
7.6%
0 95
 
7.5%
8 90
 
7.1%
7 75
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
K 795
47.3%
S 567
33.8%
R 142
 
8.5%
B 126
 
7.5%
W 40
 
2.4%
F 9
 
0.5%
Math Symbol
ValueCountFrequency (%)
+ 8
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1679
56.9%
Common 1274
43.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 410
32.2%
3 106
 
8.3%
2 99
 
7.8%
4 97
 
7.6%
5 97
 
7.6%
6 97
 
7.6%
9 96
 
7.5%
0 95
 
7.5%
8 90
 
7.1%
7 75
 
5.9%
Other values (2) 12
 
0.9%
Latin
ValueCountFrequency (%)
K 795
47.3%
S 567
33.8%
R 142
 
8.5%
B 126
 
7.5%
W 40
 
2.4%
F 9
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2953
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
K 795
26.9%
S 567
19.2%
1 410
13.9%
R 142
 
4.8%
B 126
 
4.3%
3 106
 
3.6%
2 99
 
3.4%
4 97
 
3.3%
5 97
 
3.3%
6 97
 
3.3%
Other values (8) 417
14.1%

취급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct443
Distinct (%)44.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3505527 × 109
Minimum0
Maximum2.51845 × 1010
Zeros516
Zeros (%)51.6%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T19:47:26.591429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33.25775 × 109
95-th percentile1.04555 × 1010
Maximum2.51845 × 1010
Range2.51845 × 1010
Interquartile range (IQR)3.25775 × 109

Descriptive statistics

Standard deviation3.8761778 × 109
Coefficient of variation (CV)1.6490496
Kurtosis5.8072463
Mean2.3505527 × 109
Median Absolute Deviation (MAD)0
Skewness2.254628
Sum2.3505527 × 1012
Variance1.5024755 × 1019
MonotonicityNot monotonic
2023-12-12T19:47:26.799962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 516
51.6%
95000000 5
 
0.5%
7640000000 3
 
0.3%
1599200000 2
 
0.2%
3163000000 2
 
0.2%
7684000000 2
 
0.2%
1745000000 2
 
0.2%
2910500000 2
 
0.2%
10449500000 2
 
0.2%
3724500000 2
 
0.2%
Other values (433) 462
46.2%
ValueCountFrequency (%)
0 516
51.6%
30000000 1
 
0.1%
83000000 1
 
0.1%
90000000 1
 
0.1%
95000000 5
 
0.5%
99000000 1
 
0.1%
100800000 1
 
0.1%
127500000 1
 
0.1%
153000000 1
 
0.1%
174800000 1
 
0.1%
ValueCountFrequency (%)
25184500000 1
0.1%
22196600000 1
0.1%
21559500000 1
0.1%
21372600000 1
0.1%
20817324000 1
0.1%
20577500000 1
0.1%
19426500000 1
0.1%
19074450000 1
0.1%
19007250000 1
0.1%
17873500000 1
0.1%

보증잔액기업수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct123
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.278
Minimum0
Maximum264
Zeros484
Zeros (%)48.4%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T19:47:26.996064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q330
95-th percentile90
Maximum264
Range264
Interquartile range (IQR)30

Descriptive statistics

Standard deviation34.658447
Coefficient of variation (CV)1.7091649
Kurtosis9.4127089
Mean20.278
Median Absolute Deviation (MAD)1
Skewness2.6706112
Sum20278
Variance1201.2079
MonotonicityNot monotonic
2023-12-12T19:47:27.180209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 484
48.4%
5 20
 
2.0%
6 18
 
1.8%
1 18
 
1.8%
10 16
 
1.6%
3 16
 
1.6%
7 13
 
1.3%
11 13
 
1.3%
9 11
 
1.1%
12 11
 
1.1%
Other values (113) 380
38.0%
ValueCountFrequency (%)
0 484
48.4%
1 18
 
1.8%
2 8
 
0.8%
3 16
 
1.6%
4 11
 
1.1%
5 20
 
2.0%
6 18
 
1.8%
7 13
 
1.3%
8 6
 
0.6%
9 11
 
1.1%
ValueCountFrequency (%)
264 1
0.1%
232 1
0.1%
228 1
0.1%
219 1
0.1%
197 1
0.1%
194 1
0.1%
189 1
0.1%
186 1
0.1%
170 1
0.1%
162 1
0.1%

보증잔액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct508
Distinct (%)50.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2087042 × 109
Minimum0
Maximum8.7176109 × 1010
Zeros484
Zeros (%)48.4%
Negative0
Negative (%)0.0%
Memory size8.9 KiB
2023-12-12T19:47:27.374809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median84560000
Q35.084802 × 109
95-th percentile2.7345081 × 1010
Maximum8.7176109 × 1010
Range8.7176109 × 1010
Interquartile range (IQR)5.084802 × 109

Descriptive statistics

Standard deviation1.0503225 × 1010
Coefficient of variation (CV)2.0164757
Kurtosis12.271252
Mean5.2087042 × 109
Median Absolute Deviation (MAD)84560000
Skewness3.1143019
Sum5.2087042 × 1012
Variance1.1031774 × 1020
MonotonicityNot monotonic
2023-12-12T19:47:27.566137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 484
48.4%
20107962039 2
 
0.2%
2745628574 2
 
0.2%
215696000 2
 
0.2%
12538665000 2
 
0.2%
506380000 2
 
0.2%
3660700000 2
 
0.2%
761055000 2
 
0.2%
2453086848 2
 
0.2%
25232451876 2
 
0.2%
Other values (498) 498
49.8%
ValueCountFrequency (%)
0 484
48.4%
4560001 1
 
0.1%
11200000 1
 
0.1%
12000000 1
 
0.1%
12800000 1
 
0.1%
15725000 1
 
0.1%
22000000 1
 
0.1%
27920000 1
 
0.1%
41200000 1
 
0.1%
44720000 1
 
0.1%
ValueCountFrequency (%)
87176109288 1
0.1%
70769371028 1
0.1%
65617679375 1
0.1%
65279551634 1
0.1%
64993431753 1
0.1%
61581020006 1
0.1%
58554448195 1
0.1%
58494408992 1
0.1%
52009383793 1
0.1%
51148300847 1
0.1%

DM최종수정일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
2021-09-15 0:00
1000 

Length

Max length15
Median length15
Mean length15
Min length15

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-09-15 0:00
2nd row2021-09-15 0:00
3rd row2021-09-15 0:00
4th row2021-09-15 0:00
5th row2021-09-15 0:00

Common Values

ValueCountFrequency (%)
2021-09-15 0:00 1000
100.0%

Length

2023-12-12T19:47:27.731162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:47:27.853735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-09-15 1000
50.0%
0:00 1000
50.0%

Interactions

2023-12-12T19:47:23.242999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:22.345978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:22.789721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:23.375692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:22.493619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:22.947789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:23.518616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:22.651046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:23.102188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:47:27.935457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계기업규모코드신규보증취급구분코드통계신용등급구분코드브릿지론구분코드신용등급값취급금액보증잔액기업수보증잔액
통계기업규모코드1.0000.0060.0000.0000.1310.0000.0000.000
신규보증취급구분코드0.0061.0000.0000.0160.6520.6410.5660.473
통계신용등급구분코드0.0000.0001.0000.0000.5200.0000.0490.096
브릿지론구분코드0.0000.0160.0001.0000.0830.0000.1010.130
신용등급값0.1310.6520.5200.0831.0000.4320.3780.352
취급금액0.0000.6410.0000.0000.4321.0000.3090.176
보증잔액기업수0.0000.5660.0490.1010.3780.3091.0000.954
보증잔액0.0000.4730.0960.1300.3520.1760.9541.000
2023-12-12T19:47:28.099874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
신규보증취급구분코드브릿지론구분코드통계신용등급구분코드통계기업규모코드
신규보증취급구분코드1.0000.0260.0000.010
브릿지론구분코드0.0261.0000.0000.000
통계신용등급구분코드0.0000.0001.0000.000
통계기업규모코드0.0100.0000.0001.000
2023-12-12T19:47:28.242287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
취급금액보증잔액기업수보증잔액통계기업규모코드신규보증취급구분코드통계신용등급구분코드브릿지론구분코드
취급금액1.000-0.857-0.8570.0000.4870.0000.000
보증잔액기업수-0.8571.0000.9790.0000.4080.0370.077
보증잔액-0.8570.9791.0000.0000.3210.0730.099
통계기업규모코드0.0000.0000.0001.0000.0100.0000.000
신규보증취급구분코드0.4870.4080.3210.0101.0000.0000.026
통계신용등급구분코드0.0000.0370.0730.0000.0001.0000.000
브릿지론구분코드0.0000.0770.0990.0000.0260.0001.000

Missing values

2023-12-12T19:47:23.702040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:47:23.960281image/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

BAS_DT부점코드통계기업규모코드신규보증취급구분코드통계신용등급구분코드브릿지론구분코드신용등급값취급금액보증잔액기업수보증잔액DM최종수정일자
02021-09-15 0:00022111SB70239863000052021-09-15 0:00
12021-09-15 0:00021720KS5432000000002021-09-15 0:00
22021-09-15 0:00021720KS3582000000002021-09-15 0:00
32021-09-15 0:00021310KR11895000000002021-09-15 0:00
42021-09-15 0:00022120KS6038113519325682021-09-15 0:00
52021-09-15 0:00022110KS601282605000002021-09-15 0:00
62021-09-15 0:00022120S4+02279200002021-09-15 0:00
72021-09-15 0:00021710KS105996500000002021-09-15 0:00
82021-09-15 0:00022120K1303094717920002021-09-15 0:00
92021-09-15 0:00022121SB90104453600002021-09-15 0:00
BAS_DT부점코드통계기업규모코드신규보증취급구분코드통계신용등급구분코드브릿지론구분코드신용등급값취급금액보증잔액기업수보증잔액DM최종수정일자
9902021-09-15 0:00022121SB202622224500002021-09-15 0:00
9912021-09-15 0:00022121K80264871761092882021-09-15 0:00
9922021-09-15 0:00021720KS115101500000002021-09-15 0:00
9932021-09-15 0:00021710KS610673000000002021-09-15 0:00
9942021-09-15 0:00021710KS113401200000002021-09-15 0:00
9952021-09-15 0:00022111KS8048161256067982021-09-15 0:00
9962021-09-15 0:00022120K12047141153220002021-09-15 0:00
9972021-09-15 0:00022110KR4036143189075002021-09-15 0:00
9982021-09-15 0:00022110KR12091160211705992021-09-15 0:00
9992021-09-15 0:00022111SB802511144789442021-09-15 0:00