Overview

Dataset statistics

Number of variables14
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory56.8 KiB
Average record size in memory116.3 B

Variable types

Text5
Categorical7
Boolean1
Numeric1

Dataset

Description해당 파일 데이터는 신용보증기금의 고객기본정보고객업종정보에 대해 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15093069/fileData.do

Alerts

실제변동일자 has constant value ""Constant
유효개시일자 has constant value ""Constant
유효종료일자 has constant value ""Constant
최종여부 is highly overall correlated with 업종차수High correlation
업종차수 is highly overall correlated with 최종여부High correlation
처리직원번호 is highly overall correlated with 고객변동사유코드High correlation
고객변동사유코드 is highly overall correlated with 처리직원번호High correlation
이력일련번호 is highly imbalanced (94.7%)Imbalance
고객변동사유코드 is highly imbalanced (53.6%)Imbalance
최종수정수 is highly imbalanced (68.6%)Imbalance

Reproduction

Analysis started2023-12-12 02:46:09.311547
Analysis finished2023-12-12 02:46:10.192555
Duration0.88 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

고객ID
Text

Distinct210
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T11:46:10.415634image/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

Unique2 ?
Unique (%)0.4%

Sample

1st row9dkL05X954
2nd row9dkL05X954
3rd row9dkL05X954
4th row9dnS0EPfUe
5th row9dnS0EPfUe
ValueCountFrequency (%)
9dnszsr5x1 6
 
1.2%
9dns0gjdwa 3
 
0.6%
9cswqxzyw0 3
 
0.6%
9dnsnmaugk 3
 
0.6%
9dns0epfue 3
 
0.6%
9dnsux6fuh 3
 
0.6%
9dnmmch1t5 3
 
0.6%
9dnmy4j7xq 3
 
0.6%
9dhnhtmhrh 3
 
0.6%
9dnsw6cbv8 3
 
0.6%
Other values (200) 467
93.4%
2023-12-12T11:46:10.924615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 534
 
10.7%
d 475
 
9.5%
n 416
 
8.3%
S 340
 
6.8%
a 104
 
2.1%
O 103
 
2.1%
c 94
 
1.9%
M 79
 
1.6%
V 78
 
1.6%
N 78
 
1.6%
Other values (52) 2699
54.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2187
43.7%
Uppercase Letter 1822
36.4%
Decimal Number 991
19.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 475
21.7%
n 416
19.0%
a 104
 
4.8%
c 94
 
4.3%
k 76
 
3.5%
o 69
 
3.2%
f 68
 
3.1%
h 62
 
2.8%
r 61
 
2.8%
b 58
 
2.7%
Other values (16) 704
32.2%
Uppercase Letter
ValueCountFrequency (%)
S 340
18.7%
O 103
 
5.7%
M 79
 
4.3%
V 78
 
4.3%
N 78
 
4.3%
Z 77
 
4.2%
X 74
 
4.1%
Q 73
 
4.0%
D 73
 
4.0%
L 70
 
3.8%
Other values (16) 777
42.6%
Decimal Number
ValueCountFrequency (%)
9 534
53.9%
7 63
 
6.4%
0 62
 
6.3%
5 62
 
6.3%
6 57
 
5.8%
1 52
 
5.2%
3 47
 
4.7%
8 41
 
4.1%
4 39
 
3.9%
2 34
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 4009
80.2%
Common 991
 
19.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 475
 
11.8%
n 416
 
10.4%
S 340
 
8.5%
a 104
 
2.6%
O 103
 
2.6%
c 94
 
2.3%
M 79
 
2.0%
V 78
 
1.9%
N 78
 
1.9%
Z 77
 
1.9%
Other values (42) 2165
54.0%
Common
ValueCountFrequency (%)
9 534
53.9%
7 63
 
6.4%
0 62
 
6.3%
5 62
 
6.3%
6 57
 
5.8%
1 52
 
5.2%
3 47
 
4.7%
8 41
 
4.1%
4 39
 
3.9%
2 34
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 534
 
10.7%
d 475
 
9.5%
n 416
 
8.3%
S 340
 
6.8%
a 104
 
2.1%
O 103
 
2.1%
c 94
 
1.9%
M 79
 
1.6%
V 78
 
1.6%
N 78
 
1.6%
Other values (52) 2699
54.0%

업종차수
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
10
210 
9
209 
8
81 

Length

Max length2
Median length1
Mean length1.42
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row9
3rd row10
4th row8
5th row9

Common Values

ValueCountFrequency (%)
10 210
42.0%
9 209
41.8%
8 81
 
16.2%

Length

2023-12-12T11:46:11.096053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:46:11.250352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10 210
42.0%
9 209
41.8%
8 81
 
16.2%

이력일련번호
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
497 
2
 
3

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 (%)
1 497
99.4%
2 3
 
0.6%

Length

2023-12-12T11:46:11.362510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:46:11.454056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 497
99.4%
2 3
 
0.6%

업종코드
Text

Distinct233
Distinct (%)46.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T11:46:11.711086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters3000
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique101 ?
Unique (%)20.2%

Sample

1st rowD28111
2nd rowC25111
3rd rowC25111
4th rowK65999
5th rowK64999
ValueCountFrequency (%)
i56111 37
 
7.4%
s96999 12
 
2.4%
g47312 8
 
1.6%
s96112 8
 
1.6%
g47822 6
 
1.2%
i56193 6
 
1.2%
l68221 6
 
1.2%
g47911 6
 
1.2%
g47912 6
 
1.2%
g46329 6
 
1.2%
Other values (223) 399
79.8%
2023-12-12T11:46:12.188496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 627
20.9%
2 383
12.8%
9 305
10.2%
4 243
 
8.1%
6 221
 
7.4%
5 215
 
7.2%
3 166
 
5.5%
G 148
 
4.9%
0 141
 
4.7%
7 114
 
3.8%
Other values (17) 437
14.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2500
83.3%
Uppercase Letter 500
 
16.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 148
29.6%
I 75
15.0%
C 71
14.2%
S 36
 
7.2%
L 31
 
6.2%
D 23
 
4.6%
F 22
 
4.4%
P 19
 
3.8%
J 16
 
3.2%
R 14
 
2.8%
Other values (7) 45
 
9.0%
Decimal Number
ValueCountFrequency (%)
1 627
25.1%
2 383
15.3%
9 305
12.2%
4 243
 
9.7%
6 221
 
8.8%
5 215
 
8.6%
3 166
 
6.6%
0 141
 
5.6%
7 114
 
4.6%
8 85
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common 2500
83.3%
Latin 500
 
16.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 148
29.6%
I 75
15.0%
C 71
14.2%
S 36
 
7.2%
L 31
 
6.2%
D 23
 
4.6%
F 22
 
4.4%
P 19
 
3.8%
J 16
 
3.2%
R 14
 
2.8%
Other values (7) 45
 
9.0%
Common
ValueCountFrequency (%)
1 627
25.1%
2 383
15.3%
9 305
12.2%
4 243
 
9.7%
6 221
 
8.8%
5 215
 
8.6%
3 166
 
6.6%
0 141
 
5.6%
7 114
 
4.6%
8 85
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 627
20.9%
2 383
12.8%
9 305
10.2%
4 243
 
8.1%
6 221
 
7.4%
5 215
 
7.2%
3 166
 
5.5%
G 148
 
4.9%
0 141
 
4.7%
7 114
 
3.8%
Other values (17) 437
14.6%

고객변동사유코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
385 
1
100 
7
 
12
6
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
385
77.0%
1 100
 
20.0%
7 12
 
2.4%
6 3
 
0.6%

Length

2023-12-12T11:46:12.356300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:46:12.474344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 100
87.0%
7 12
 
10.4%
6 3
 
2.6%

실제변동일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
00:00.0
500 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row00:00.0
2nd row00:00.0
3rd row00:00.0
4th row00:00.0
5th row00:00.0

Common Values

ValueCountFrequency (%)
00:00.0 500
100.0%

Length

2023-12-12T11:46:12.591350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:46:12.704698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00.0 500
100.0%

최종여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
290 
True
210 
ValueCountFrequency (%)
False 290
58.0%
True 210
42.0%
2023-12-12T11:46:12.799818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

유효개시일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
00:00.0
500 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row00:00.0
2nd row00:00.0
3rd row00:00.0
4th row00:00.0
5th row00:00.0

Common Values

ValueCountFrequency (%)
00:00.0 500
100.0%

Length

2023-12-12T11:46:12.900911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:46:12.991478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00.0 500
100.0%

유효종료일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
00:00.0
500 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row00:00.0
2nd row00:00.0
3rd row00:00.0
4th row00:00.0
5th row00:00.0

Common Values

ValueCountFrequency (%)
00:00.0 500
100.0%

Length

2023-12-12T11:46:13.083187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:46:13.177656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00.0 500
100.0%

최종수정수
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
442 
2
45 
3
 
10
5
 
3

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 (%)
1 442
88.4%
2 45
 
9.0%
3 10
 
2.0%
5 3
 
0.6%

Length

2023-12-12T11:46:13.291719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:46:13.409931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 442
88.4%
2 45
 
9.0%
3 10
 
2.0%
5 3
 
0.6%

처리시각
Text

Distinct218
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T11:46:13.798415image/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

Unique13 ?
Unique (%)2.6%

Sample

1st row37:22.4
2nd row37:22.4
3rd row37:22.4
4th row36:49.3
5th row36:49.3
ValueCountFrequency (%)
17:30.7 5
 
1.0%
22:26.2 3
 
0.6%
09:57.6 3
 
0.6%
20:14.7 3
 
0.6%
12:02.7 3
 
0.6%
13:53.7 3
 
0.6%
17:30.0 3
 
0.6%
20:29.7 3
 
0.6%
22:22.4 3
 
0.6%
23:47.9 3
 
0.6%
Other values (208) 468
93.6%
2023-12-12T11:46:14.332568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
2 350
10.0%
3 328
9.4%
1 313
8.9%
4 293
8.4%
5 288
8.2%
0 281
8.0%
7 205
5.9%
8 180
 
5.1%
Other values (2) 262
7.5%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 350
14.0%
3 328
13.1%
1 313
12.5%
4 293
11.7%
5 288
11.5%
0 281
11.2%
7 205
8.2%
8 180
7.2%
6 134
 
5.4%
9 128
 
5.1%
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%
2 350
10.0%
3 328
9.4%
1 313
8.9%
4 293
8.4%
5 288
8.2%
0 281
8.0%
7 205
5.9%
8 180
 
5.1%
Other values (2) 262
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
2 350
10.0%
3 328
9.4%
1 313
8.9%
4 293
8.4%
5 288
8.2%
0 281
8.0%
7 205
5.9%
8 180
 
5.1%
Other values (2) 262
7.5%

처리직원번호
Real number (ℝ)

HIGH CORRELATION 

Distinct76
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53337.996
Minimum2962
Maximum99023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T11:46:14.774355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2962
5-th percentile3590
Q14528
median99001
Q399007
95-th percentile99023
Maximum99023
Range96061
Interquartile range (IQR)94479

Descriptive statistics

Standard deviation47208.588
Coefficient of variation (CV)0.88508365
Kurtosis-2.0031049
Mean53337.996
Median Absolute Deviation (MAD)22
Skewness-0.0647714
Sum26668998
Variance2.2286508 × 109
MonotonicityNot monotonic
2023-12-12T11:46:14.928334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99016 50
 
10.0%
99001 48
 
9.6%
99002 46
 
9.2%
99007 44
 
8.8%
99023 42
 
8.4%
99006 22
 
4.4%
5617 9
 
1.8%
3746 9
 
1.8%
3769 6
 
1.2%
5769 6
 
1.2%
Other values (66) 218
43.6%
ValueCountFrequency (%)
2962 3
0.6%
3071 3
0.6%
3279 3
0.6%
3353 3
0.6%
3471 3
0.6%
3504 6
1.2%
3553 3
0.6%
3590 3
0.6%
3621 4
0.8%
3734 3
0.6%
ValueCountFrequency (%)
99023 42
8.4%
99016 50
10.0%
99015 4
 
0.8%
99014 2
 
0.4%
99007 44
8.8%
99006 22
4.4%
99002 46
9.2%
99001 48
9.6%
6177 6
 
1.2%
6147 3
 
0.6%

최초처리시각
Text

Distinct224
Distinct (%)44.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T11:46:15.252884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length7
Mean length7.038
Min length7

Characters and Unicode

Total characters3519
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)5.8%

Sample

1st row37:22.4
2nd row37:22.4
3rd row37:22.4
4th row36:49.3
5th row36:49.3
ValueCountFrequency (%)
26:16.9 9
 
1.8%
25:00.9 6
 
1.2%
17:30.7 5
 
1.0%
22:22.4 3
 
0.6%
20:14.7 3
 
0.6%
34:41.8 3
 
0.6%
17:04.8 3
 
0.6%
17:30.0 3
 
0.6%
20:29.7 3
 
0.6%
46:00.7 3
 
0.6%
Other values (215) 460
91.8%
2023-12-12T11:46:15.774402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 501
14.2%
. 500
14.2%
2 333
9.5%
1 325
9.2%
3 307
8.7%
5 306
8.7%
0 299
8.5%
4 292
8.3%
7 199
 
5.7%
8 164
 
4.7%
Other values (4) 293
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2515
71.5%
Other Punctuation 1001
 
28.4%
Dash Punctuation 2
 
0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 333
13.2%
1 325
12.9%
3 307
12.2%
5 306
12.2%
0 299
11.9%
4 292
11.6%
7 199
7.9%
8 164
6.5%
6 152
6.0%
9 138
5.5%
Other Punctuation
ValueCountFrequency (%)
: 501
50.0%
. 500
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3519
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
: 501
14.2%
. 500
14.2%
2 333
9.5%
1 325
9.2%
3 307
8.7%
5 306
8.7%
0 299
8.5%
4 292
8.3%
7 199
 
5.7%
8 164
 
4.7%
Other values (4) 293
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3519
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 501
14.2%
. 500
14.2%
2 333
9.5%
1 325
9.2%
3 307
8.7%
5 306
8.7%
0 299
8.5%
4 292
8.3%
7 199
 
5.7%
8 164
 
4.7%
Other values (4) 293
8.3%

최초처리직원번호
Text

Distinct90
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T11:46:16.118048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.49
Min length4

Characters and Unicode

Total characters2245
Distinct characters11
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

Unique11 ?
Unique (%)2.2%

Sample

1st row4147
2nd row4147
3rd row4147
4th row5814
5th row5814
ValueCountFrequency (%)
99016 46
 
9.2%
99002 44
 
8.8%
99001 42
 
8.4%
99007 42
 
8.4%
99023 38
 
7.6%
99006 22
 
4.4%
3746 9
 
1.8%
5176 9
 
1.8%
5617 7
 
1.4%
4530 6
 
1.2%
Other values (79) 234
46.9%
2023-12-12T11:46:16.503229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 575
25.6%
0 479
21.3%
1 182
 
8.1%
3 181
 
8.1%
5 177
 
7.9%
4 159
 
7.1%
7 155
 
6.9%
6 154
 
6.9%
2 134
 
6.0%
8 44
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2240
99.8%
Space Separator 5
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 575
25.7%
0 479
21.4%
1 182
 
8.1%
3 181
 
8.1%
5 177
 
7.9%
4 159
 
7.1%
7 155
 
6.9%
6 154
 
6.9%
2 134
 
6.0%
8 44
 
2.0%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2245
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 575
25.6%
0 479
21.3%
1 182
 
8.1%
3 181
 
8.1%
5 177
 
7.9%
4 159
 
7.1%
7 155
 
6.9%
6 154
 
6.9%
2 134
 
6.0%
8 44
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2245
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 575
25.6%
0 479
21.3%
1 182
 
8.1%
3 181
 
8.1%
5 177
 
7.9%
4 159
 
7.1%
7 155
 
6.9%
6 154
 
6.9%
2 134
 
6.0%
8 44
 
2.0%

Interactions

2023-12-12T11:46:09.765901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:46:16.598736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종차수이력일련번호고객변동사유코드최종여부최종수정수처리직원번호최초처리직원번호
업종차수1.0000.0000.1681.0000.0000.2790.000
이력일련번호0.0001.0000.0000.0000.0000.0500.757
고객변동사유코드0.1680.0001.0000.0680.6070.7750.995
최종여부1.0000.0000.0681.0000.0000.2440.000
최종수정수0.0000.0000.6070.0001.0000.0720.975
처리직원번호0.2790.0500.7750.2440.0721.0000.997
최초처리직원번호0.0000.7570.9950.0000.9750.9971.000
2023-12-12T11:46:16.716932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이력일련번호고객변동사유코드최종여부업종차수최종수정수
이력일련번호1.0000.0000.0000.0000.000
고객변동사유코드0.0001.0000.0450.1590.275
최종여부0.0000.0451.0000.9990.000
업종차수0.0000.1590.9991.0000.000
최종수정수0.0000.2750.0000.0001.000
2023-12-12T11:46:16.815242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처리직원번호업종차수이력일련번호고객변동사유코드최종여부최종수정수
처리직원번호1.0000.4500.0310.5600.1570.045
업종차수0.4501.0000.0000.1590.9990.000
이력일련번호0.0310.0001.0000.0000.0000.000
고객변동사유코드0.5600.1590.0001.0000.0450.275
최종여부0.1570.9990.0000.0451.0000.000
최종수정수0.0450.0000.0000.2750.0001.000

Missing values

2023-12-12T11:46:09.894820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:46:10.119943image/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

고객ID업종차수이력일련번호업종코드고객변동사유코드실제변동일자최종여부유효개시일자유효종료일자최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
09dkL05X95481D28111100:00.0N00:00.000:00.0137:22.4414737:22.44147
19dkL05X95491C25111100:00.0N00:00.000:00.0137:22.4414737:22.44147
29dkL05X954101C25111100:00.0Y00:00.000:00.0137:22.4414737:22.44147
39dnS0EPfUe81K6599900:00.0N00:00.000:00.0136:49.3581436:49.35814
49dnS0EPfUe91K6499900:00.0N00:00.000:00.0136:49.3581436:49.35814
59dnS0EPfUe101K6499900:00.0Y00:00.000:00.0136:49.3581436:49.35814
69dnSXT5WVf91S9612900:00.0N00:00.000:00.0136:31.79902336:31.799023
79dnSXT5WVf101S9612900:00.0Y00:00.000:00.0136:31.79902336:31.799023
89dnSZnl3L291I5622000:00.0N00:00.000:00.0135:27.19900235:27.199002
99dnSZnl3L2101I5622100:00.0Y00:00.000:00.0135:27.19900235:27.199002
고객ID업종차수이력일련번호업종코드고객변동사유코드실제변동일자최종여부유효개시일자유효종료일자최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
4909dnSE9NoOt101G4781300:00.0Y00:00.000:00.0115:34.29900715:34.299007
4919dnSNqOqVF81G5263300:00.0N00:00.000:00.0114:52.8521414:52.85214
4929dnSNqOqVF91G4782200:00.0N00:00.000:00.0114:52.8521414:52.85214
4939dnSNqOqVF101G4782200:00.0Y00:00.000:00.0114:52.7521414:52.75214
494aaaaaawBQJ81D21211100:00.0N00:00.000:00.0311:53.235040001-01-01 00:00:00.000000
495aaaaaawBQJ91C17210100:00.0N00:00.000:00.0211:53.2350411:39.24725
496aaaaaawBQJ101C17212100:00.0Y00:00.000:00.0111:53.2350411:53.23504
4979dnSM8c2lH81Q87111100:00.0N00:00.000:00.0111:23.6607811:23.66078
4989dnSM8c2lH91J59111100:00.0N00:00.000:00.0111:23.6607811:23.66078
4999dnSM8c2lH101J59111100:00.0Y00:00.000:00.0111:23.6607811:23.66078