Overview

Dataset statistics

Number of variables13
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory54.3 KiB
Average record size in memory111.3 B

Variable types

Text3
Numeric5
Categorical4
Boolean1

Dataset

Description해당 파일 데이터는 신용보증기금의 거래관리항목정보에 대해 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15092781/fileData.do

Alerts

삭제여부 is highly overall correlated with 최종수정수High correlation
최종수정수 is highly overall correlated with 삭제여부High correlation
일련번호 is highly overall correlated with 관리항목일련번호 and 2 other fieldsHigh correlation
관리항목일련번호 is highly overall correlated with 일련번호 and 3 other fieldsHigh correlation
관리항목값 is highly overall correlated with 관리항목일련번호 and 1 other fieldsHigh correlation
처리직원번호 is highly overall correlated with 최초처리직원번호High correlation
최초처리직원번호 is highly overall correlated with 처리직원번호High correlation
관리항목코드 is highly overall correlated with 일련번호 and 2 other fieldsHigh correlation
관리항목값명 is highly overall correlated with 일련번호 and 3 other fieldsHigh correlation
거래분개내역일련번호 is highly imbalanced (52.8%)Imbalance
삭제여부 is highly imbalanced (77.6%)Imbalance
최종수정수 is highly imbalanced (77.6%)Imbalance
관리항목값 has 16 (3.2%) zerosZeros

Reproduction

Analysis started2023-12-12 15:44:28.695582
Analysis finished2023-12-12 15:44:32.892940
Duration4.2 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

거래공통내역ID
Text

Distinct137
Distinct (%)27.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T00:44:33.161282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5000
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row9dnOuEjAAJ
2nd row9dnOuEjAAJ
3rd row9dnOuoDV21
4th row9dnOuoDV21
5th row9dnOujFbo9
ValueCountFrequency (%)
9dltx2sokn 12
 
2.4%
9dltw2suum 12
 
2.4%
9dnoqgwgxn 12
 
2.4%
9dlmgyngsb 8
 
1.6%
9dnotbicqj 8
 
1.6%
9djnbvm3kb 4
 
0.8%
9dlkumcz7g 4
 
0.8%
9djm4han2x 4
 
0.8%
9dltvnwrjm 4
 
0.8%
9dltvqkarw 4
 
0.8%
Other values (127) 428
85.6%
2023-12-13T00:44:33.651447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 550
 
11.0%
9 537
 
10.7%
l 410
 
8.2%
O 218
 
4.4%
t 191
 
3.8%
k 156
 
3.1%
W 136
 
2.7%
V 133
 
2.7%
j 125
 
2.5%
n 111
 
2.2%
Other values (52) 2433
48.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2502
50.0%
Uppercase Letter 1605
32.1%
Decimal Number 893
 
17.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 550
22.0%
l 410
16.4%
t 191
 
7.6%
k 156
 
6.2%
j 125
 
5.0%
n 111
 
4.4%
g 89
 
3.6%
u 74
 
3.0%
m 70
 
2.8%
f 68
 
2.7%
Other values (16) 658
26.3%
Uppercase Letter
ValueCountFrequency (%)
O 218
 
13.6%
W 136
 
8.5%
V 133
 
8.3%
U 93
 
5.8%
X 87
 
5.4%
Y 84
 
5.2%
N 83
 
5.2%
K 68
 
4.2%
I 61
 
3.8%
T 58
 
3.6%
Other values (16) 584
36.4%
Decimal Number
ValueCountFrequency (%)
9 537
60.1%
7 58
 
6.5%
2 57
 
6.4%
6 40
 
4.5%
3 39
 
4.4%
0 38
 
4.3%
4 38
 
4.3%
1 36
 
4.0%
5 26
 
2.9%
8 24
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 4107
82.1%
Common 893
 
17.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 550
 
13.4%
l 410
 
10.0%
O 218
 
5.3%
t 191
 
4.7%
k 156
 
3.8%
W 136
 
3.3%
V 133
 
3.2%
j 125
 
3.0%
n 111
 
2.7%
U 93
 
2.3%
Other values (42) 1984
48.3%
Common
ValueCountFrequency (%)
9 537
60.1%
7 58
 
6.5%
2 57
 
6.4%
6 40
 
4.5%
3 39
 
4.4%
0 38
 
4.3%
4 38
 
4.3%
1 36
 
4.0%
5 26
 
2.9%
8 24
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 550
 
11.0%
9 537
 
10.7%
l 410
 
8.2%
O 218
 
4.4%
t 191
 
3.8%
k 156
 
3.1%
W 136
 
2.7%
V 133
 
2.7%
j 125
 
2.5%
n 111
 
2.2%
Other values (52) 2433
48.7%

일련번호
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.824
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T00:44:33.833632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile4
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1151283
Coefficient of variation (CV)0.61136417
Kurtosis4.6784605
Mean1.824
Median Absolute Deviation (MAD)1
Skewness1.944849
Sum912
Variance1.243511
MonotonicityNot monotonic
2023-12-13T00:44:33.963164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 244
48.8%
2 174
34.8%
3 36
 
7.2%
4 30
 
6.0%
6 7
 
1.4%
5 7
 
1.4%
8 1
 
0.2%
7 1
 
0.2%
ValueCountFrequency (%)
1 244
48.8%
2 174
34.8%
3 36
 
7.2%
4 30
 
6.0%
5 7
 
1.4%
6 7
 
1.4%
7 1
 
0.2%
8 1
 
0.2%
ValueCountFrequency (%)
8 1
 
0.2%
7 1
 
0.2%
6 7
 
1.4%
5 7
 
1.4%
4 30
 
6.0%
3 36
 
7.2%
2 174
34.8%
1 244
48.8%

거래분개내역일련번호
Categorical

IMBALANCE 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
253 
2
241 
3
 
4
4
 
1
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
1 253
50.6%
2 241
48.2%
3 4
 
0.8%
4 1
 
0.2%
5 1
 
0.2%

Length

2023-12-13T00:44:34.108166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:44:34.243967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 253
50.6%
2 241
48.2%
3 4
 
0.8%
4 1
 
0.2%
5 1
 
0.2%

관리항목일련번호
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.92
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T00:44:34.368024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q13
median4
Q34
95-th percentile7
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3657711
Coefficient of variation (CV)0.34841099
Kurtosis2.4788046
Mean3.92
Median Absolute Deviation (MAD)1
Skewness1.5097822
Sum1960
Variance1.8653307
MonotonicityNot monotonic
2023-12-13T00:44:34.513423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
3 224
44.8%
4 170
34.0%
7 29
 
5.8%
5 29
 
5.8%
6 23
 
4.6%
2 7
 
1.4%
1 6
 
1.2%
9 6
 
1.2%
8 6
 
1.2%
ValueCountFrequency (%)
1 6
 
1.2%
2 7
 
1.4%
3 224
44.8%
4 170
34.0%
5 29
 
5.8%
6 23
 
4.6%
7 29
 
5.8%
8 6
 
1.2%
9 6
 
1.2%
ValueCountFrequency (%)
9 6
 
1.2%
8 6
 
1.2%
7 29
 
5.8%
6 23
 
4.6%
5 29
 
5.8%
4 170
34.0%
3 224
44.8%
2 7
 
1.4%
1 6
 
1.2%

관리항목코드
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
C124
198 
C303
141 
C232
29 
C201
29 
C202
29 
Other values (9)
74 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st rowC124
2nd rowC259
3rd rowC124
4th rowC259
5th rowC124

Common Values

ValueCountFrequency (%)
C124 198
39.6%
C303 141
28.2%
C232 29
 
5.8%
C201 29
 
5.8%
C202 29
 
5.8%
C177 23
 
4.6%
C259 19
 
3.8%
C104 6
 
1.2%
C310 6
 
1.2%
C103 6
 
1.2%
Other values (4) 14
 
2.8%

Length

2023-12-13T00:44:34.659974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
c124 198
39.6%
c303 141
28.2%
c232 29
 
5.8%
c201 29
 
5.8%
c202 29
 
5.8%
c177 23
 
4.6%
c259 19
 
3.8%
c104 6
 
1.2%
c310 6
 
1.2%
c103 6
 
1.2%
Other values (4) 14
 
2.8%

관리항목값
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40069281
Minimum-6.663 × 108
Maximum1.8195085 × 109
Zeros16
Zeros (%)3.2%
Negative2
Negative (%)0.4%
Memory size4.5 KiB
2023-12-13T00:44:35.116613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-6.663 × 108
5-th percentile1
Q1430
median500000
Q311020300
95-th percentile12010300
Maximum1.8195085 × 109
Range2.4858085 × 109
Interquartile range (IQR)11019870

Descriptive statistics

Standard deviation2.6249901 × 108
Coefficient of variation (CV)6.5511283
Kurtosis40.489568
Mean40069281
Median Absolute Deviation (MAD)499998
Skewness6.3055386
Sum2.0034641 × 1010
Variance6.8905728 × 1016
MonotonicityNot monotonic
2023-12-13T00:44:35.286847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
880000 64
12.8%
1 46
 
9.2%
11020300 39
 
7.8%
12010300 36
 
7.2%
12010100 36
 
7.2%
11020100 30
 
6.0%
430 30
 
6.0%
2 29
 
5.8%
320000 25
 
5.0%
810000 24
 
4.8%
Other values (17) 141
28.2%
ValueCountFrequency (%)
-666300000 1
 
0.2%
-665700000 1
 
0.2%
0 16
 
3.2%
1 46
9.2%
2 29
5.8%
3 6
 
1.2%
430 30
6.0%
30000 22
4.4%
70000 10
 
2.0%
160000 8
 
1.6%
ValueCountFrequency (%)
1819508500 10
 
2.0%
1061650000 1
 
0.2%
152800000 2
 
0.4%
12040600 6
 
1.2%
12010300 36
7.2%
12010100 36
7.2%
11020300 39
7.8%
11020100 30
6.0%
880000 64
12.8%
810000 24
 
4.8%

관리항목값명
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
신한은행
64 
정기예금
48 
시장성 있음
46 
하나은행
40 
기타의단기금융상품
39 
Other values (21)
263 

Length

Max length10
Median length4
Mean length5.43
Min length1

Unique

Unique3 ?
Unique (%)0.6%

Sample

1st row대구은행
2nd row대구은행
3rd row신한은행
4th row신한은행
5th row수협은행

Common Values

ValueCountFrequency (%)
신한은행 64
12.8%
정기예금 48
 
9.6%
시장성 있음 46
 
9.2%
하나은행 40
 
8.0%
기타의단기금융상품 39
 
7.8%
기타의장기금융상품 36
 
7.2%
민간-비금융기관 30
 
6.0%
시장성 없음 29
 
5.8%
부산은행 25
 
5.0%
기업은행 22
 
4.4%
Other values (16) 121
24.2%

Length

2023-12-13T00:44:35.441295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
시장성 75
13.0%
신한은행 64
11.1%
정기예금 48
 
8.3%
있음 46
 
8.0%
하나은행 40
 
7.0%
기타의단기금융상품 39
 
6.8%
기타의장기금융상품 36
 
6.3%
민간-비금융기관 30
 
5.2%
없음 29
 
5.0%
부산은행 25
 
4.3%
Other values (17) 143
24.9%

삭제여부
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
482 
True
 
18
ValueCountFrequency (%)
False 482
96.4%
True 18
 
3.6%
2023-12-13T00:44:35.622018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

최종수정수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
482 
2
 
18

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 482
96.4%
2 18
 
3.6%

Length

2023-12-13T00:44:35.763486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:44:35.880613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 482
96.4%
2 18
 
3.6%

처리시각
Text

Distinct131
Distinct (%)26.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T00:44:36.231222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3500
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row21:24.1
2nd row21:24.1
3rd row17:32.6
4th row17:32.6
5th row16:19.0
ValueCountFrequency (%)
02:54.7 12
 
2.4%
14:34.2 12
 
2.4%
47:38.5 12
 
2.4%
50:41.4 8
 
1.6%
59:05.5 8
 
1.6%
41:04.1 8
 
1.6%
06:04.5 8
 
1.6%
51:13.5 8
 
1.6%
36:57.9 8
 
1.6%
46:44.6 8
 
1.6%
Other values (121) 408
81.6%
2023-12-13T00:44:36.893020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
4 394
11.3%
5 333
9.5%
0 325
9.3%
2 309
8.8%
3 305
8.7%
1 283
8.1%
9 165
 
4.7%
7 141
 
4.0%
Other values (2) 245
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2500
71.4%
Other Punctuation 1000
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 394
15.8%
5 333
13.3%
0 325
13.0%
2 309
12.4%
3 305
12.2%
1 283
11.3%
9 165
6.6%
7 141
 
5.6%
6 135
 
5.4%
8 110
 
4.4%
Other Punctuation
ValueCountFrequency (%)
: 500
50.0%
. 500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
4 394
11.3%
5 333
9.5%
0 325
9.3%
2 309
8.8%
3 305
8.7%
1 283
8.1%
9 165
 
4.7%
7 141
 
4.0%
Other values (2) 245
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
4 394
11.3%
5 333
9.5%
0 325
9.3%
2 309
8.8%
3 305
8.7%
1 283
8.1%
9 165
 
4.7%
7 141
 
4.0%
Other values (2) 245
7.0%

처리직원번호
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5220
Minimum3598
Maximum5852
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T00:44:37.112332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3598
5-th percentile4645
Q14645
median5538
Q35538
95-th percentile5803
Maximum5852
Range2254
Interquartile range (IQR)893

Descriptive statistics

Standard deviation499.7386
Coefficient of variation (CV)0.095735363
Kurtosis1.3439892
Mean5220
Median Absolute Deviation (MAD)314
Skewness-1.1630047
Sum2610000
Variance249738.67
MonotonicityNot monotonic
2023-12-13T00:44:37.242114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5538 192
38.4%
4645 123
24.6%
5206 90
18.0%
5803 47
 
9.4%
3598 16
 
3.2%
5852 16
 
3.2%
5227 8
 
1.6%
5134 8
 
1.6%
ValueCountFrequency (%)
3598 16
 
3.2%
4645 123
24.6%
5134 8
 
1.6%
5206 90
18.0%
5227 8
 
1.6%
5538 192
38.4%
5803 47
 
9.4%
5852 16
 
3.2%
ValueCountFrequency (%)
5852 16
 
3.2%
5803 47
 
9.4%
5538 192
38.4%
5227 8
 
1.6%
5206 90
18.0%
5134 8
 
1.6%
4645 123
24.6%
3598 16
 
3.2%

최초처리시각
Text

Distinct131
Distinct (%)26.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T00:44:37.619466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3500
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row21:24.1
2nd row21:24.1
3rd row17:32.6
4th row17:32.6
5th row16:19.0
ValueCountFrequency (%)
02:54.7 12
 
2.4%
14:34.2 12
 
2.4%
47:38.5 12
 
2.4%
50:41.4 8
 
1.6%
59:05.5 8
 
1.6%
41:04.1 8
 
1.6%
06:04.5 8
 
1.6%
51:13.5 8
 
1.6%
36:57.9 8
 
1.6%
46:44.6 8
 
1.6%
Other values (121) 408
81.6%
2023-12-13T00:44:38.221739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
4 368
10.5%
2 347
9.9%
0 341
9.7%
5 333
9.5%
1 299
8.5%
3 297
8.5%
9 153
 
4.4%
7 135
 
3.9%
Other values (2) 227
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2500
71.4%
Other Punctuation 1000
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 368
14.7%
2 347
13.9%
0 341
13.6%
5 333
13.3%
1 299
12.0%
3 297
11.9%
9 153
6.1%
7 135
 
5.4%
6 131
 
5.2%
8 96
 
3.8%
Other Punctuation
ValueCountFrequency (%)
: 500
50.0%
. 500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
4 368
10.5%
2 347
9.9%
0 341
9.7%
5 333
9.5%
1 299
8.5%
3 297
8.5%
9 153
 
4.4%
7 135
 
3.9%
Other values (2) 227
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
4 368
10.5%
2 347
9.9%
0 341
9.7%
5 333
9.5%
1 299
8.5%
3 297
8.5%
9 153
 
4.4%
7 135
 
3.9%
Other values (2) 227
6.5%

최초처리직원번호
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5222.244
Minimum3598
Maximum5852
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T00:44:38.428084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3598
5-th percentile4645
Q14645
median5538
Q35538
95-th percentile5803
Maximum5852
Range2254
Interquartile range (IQR)893

Descriptive statistics

Standard deviation498.40672
Coefficient of variation (CV)0.095439186
Kurtosis1.4049117
Mean5222.244
Median Absolute Deviation (MAD)314
Skewness-1.1797357
Sum2611122
Variance248409.25
MonotonicityNot monotonic
2023-12-13T00:44:38.565044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5538 192
38.4%
4645 121
24.2%
5206 92
18.4%
5803 47
 
9.4%
3598 16
 
3.2%
5852 16
 
3.2%
5227 8
 
1.6%
5134 8
 
1.6%
ValueCountFrequency (%)
3598 16
 
3.2%
4645 121
24.2%
5134 8
 
1.6%
5206 92
18.4%
5227 8
 
1.6%
5538 192
38.4%
5803 47
 
9.4%
5852 16
 
3.2%
ValueCountFrequency (%)
5852 16
 
3.2%
5803 47
 
9.4%
5538 192
38.4%
5227 8
 
1.6%
5206 92
18.4%
5134 8
 
1.6%
4645 121
24.2%
3598 16
 
3.2%

Interactions

2023-12-13T00:44:31.957019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:29.448048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:30.080175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:30.647105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:31.283793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:32.078497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:29.589503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:30.192728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:30.781740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:31.412936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:32.199643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:29.721486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:30.311939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:30.939689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:31.619487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:32.321050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:29.826668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:30.417169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:31.058768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:31.740820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:32.438020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:29.945529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:30.527832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:31.171585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:31.832538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:44:38.678990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호거래분개내역일련번호관리항목일련번호관리항목코드관리항목값관리항목값명삭제여부최종수정수처리직원번호최초처리직원번호
일련번호1.0000.0000.9130.9240.4840.8820.0000.0000.3850.386
거래분개내역일련번호0.0001.0000.0000.1710.0000.0000.0000.0000.3030.303
관리항목일련번호0.9130.0001.0000.9920.5120.9130.0000.0000.5640.565
관리항목코드0.9240.1710.9921.0000.5330.9320.0000.0000.6780.679
관리항목값0.4840.0000.5120.5331.0001.0000.0000.0000.4450.444
관리항목값명0.8820.0000.9130.9321.0001.0000.2260.2260.8110.812
삭제여부0.0000.0000.0000.0000.0000.2261.0000.9990.1920.234
최종수정수0.0000.0000.0000.0000.0000.2260.9991.0000.1920.234
처리직원번호0.3850.3030.5640.6780.4450.8110.1920.1921.0001.000
최초처리직원번호0.3860.3030.5650.6790.4440.8120.2340.2341.0001.000
2023-12-13T00:44:38.855811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리항목값명삭제여부거래분개내역일련번호최종수정수관리항목코드
관리항목값명1.0000.1740.0000.1740.627
삭제여부0.1741.0000.0000.9710.000
거래분개내역일련번호0.0000.0001.0000.0000.089
최종수정수0.1740.9710.0001.0000.000
관리항목코드0.6270.0000.0890.0001.000
2023-12-13T00:44:39.025642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호관리항목일련번호관리항목값처리직원번호최초처리직원번호거래분개내역일련번호관리항목코드관리항목값명삭제여부최종수정수
일련번호1.0000.849-0.3890.0770.0740.0000.7440.5960.0000.000
관리항목일련번호0.8491.000-0.5110.2050.2030.0000.9680.6400.0000.000
관리항목값-0.389-0.5111.0000.0430.0410.0000.3170.9790.0000.000
처리직원번호0.0770.2050.0431.0000.9990.1090.3910.4990.2320.232
최초처리직원번호0.0740.2030.0410.9991.0000.1090.3910.5000.2840.284
거래분개내역일련번호0.0000.0000.0000.1090.1091.0000.0890.0000.0000.000
관리항목코드0.7440.9680.3170.3910.3910.0891.0000.6270.0000.000
관리항목값명0.5960.6400.9790.4990.5000.0000.6271.0000.1740.174
삭제여부0.0000.0000.0000.2320.2840.0000.0000.1741.0000.971
최종수정수0.0000.0000.0000.2320.2840.0000.0000.1740.9711.000

Missing values

2023-12-13T00:44:32.582630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:44:32.796709image/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

거래공통내역ID일련번호거래분개내역일련번호관리항목일련번호관리항목코드관리항목값관리항목값명삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
09dnOuEjAAJ113C124310000대구은행N121:24.1464521:24.14645
19dnOuEjAAJ123C259310000대구은행N121:24.1464521:24.14645
29dnOuoDV21113C124880000신한은행N117:32.6464517:32.64645
39dnOuoDV21123C259880000신한은행N117:32.6464517:32.64645
49dnOujFbo9113C12470000수협은행N116:19.0464516:19.04645
59dnOujFbo9123C12470000수협은행N116:19.0464516:19.04645
69dnOugI78O113C124160000농협은행N115:35.6464515:35.64645
79dnOugI78O123C124160000농협은행N115:35.6464515:35.64645
89dnOudyzDu113C124500000하나은행N114:48.8464514:48.84645
99dnOudyzDu123C124500000하나은행N114:48.8464514:48.84645
거래공통내역ID일련번호거래분개내역일련번호관리항목일련번호관리항목코드관리항목값관리항목값명삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
4909djfjPAC5K113C12470000수협은행N128:26.6520628:26.65206
4919djfjPAC5K123C12470000수협은행N128:26.6520628:26.65206
4929djfjNvcEe113C124160000농협은행N127:55.8520627:55.85206
4939djfjNvcEe123C124160000농협은행N127:55.8520627:55.85206
4949djfjKYeig113C124500000하나은행N127:18.4520627:18.45206
4959djfjKYeig123C124500000하나은행N127:18.4520627:18.45206
4969djfjIDLKT113C124200000우리은행N126:43.9520626:43.95206
4979djfjIDLKT123C124200000우리은행N126:43.9520626:43.95206
4989djfjGk2TC113C124390000경남은행N126:09.9520626:09.95206
4999djfjGk2TC123C124390000경남은행N126:09.9520626:09.95206