Overview

Dataset statistics

Number of variables19
Number of observations500
Missing cells633
Missing cells (%)6.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory77.8 KiB
Average record size in memory159.3 B

Variable types

Text4
Numeric5
Categorical9
Unsupported1

Dataset

Description해당 파일 데이터는 신용보증기금의 보증상담추천정보에 대해 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15092837/fileData.do

Alerts

추천일자 has constant value ""Constant
현장조사예정일자 has constant value ""Constant
유효개시일자 has constant value ""Constant
유효종료일자 has constant value ""Constant
상담추천이첩결과코드 is highly overall correlated with 최종수정수 and 3 other fieldsHigh correlation
상담추천이첩반려사유코드 is highly overall correlated with 상담추천이첩결과코드 and 1 other fieldsHigh correlation
추천결과최초입력일자 is highly overall correlated with 이력일련번호 and 4 other fieldsHigh correlation
추천반려사유내용 is highly overall correlated with 처리직원번호 and 3 other fieldsHigh correlation
이력일련번호 is highly overall correlated with 추천결과최초입력일자High correlation
추천결과최초처리자직원번호 is highly overall correlated with 처리직원번호 and 3 other fieldsHigh correlation
최종수정수 is highly overall correlated with 상담추천이첩결과코드 and 1 other fieldsHigh correlation
처리직원번호 is highly overall correlated with 추천결과최초처리자직원번호 and 2 other fieldsHigh correlation
최초처리직원번호 is highly overall correlated with 추천결과최초처리자직원번호 and 1 other fieldsHigh correlation
이관추천구분코드 is highly overall correlated with 추천결과최초처리자직원번호High correlation
상담추천이첩반려사유코드 is highly imbalanced (96.2%)Imbalance
추천결과최초처리자직원번호 has 133 (26.6%) missing valuesMissing
정책보증센터담당자직원번호 has 500 (100.0%) missing valuesMissing
처리시각 has unique valuesUnique
정책보증센터담당자직원번호 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 08:56:14.520039
Analysis finished2023-12-12 08:56:19.216658
Duration4.7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

상담ID
Text

Distinct236
Distinct (%)47.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T17:56:19.451917image/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

Unique73 ?
Unique (%)14.6%

Sample

1st row9dnSU0Epzz
2nd row9dnDMAMEiW
3rd row9dnDMAMEiW
4th row9dnLmKBv7a
5th row9dnLmKBv7a
ValueCountFrequency (%)
9dnzrhlf7q 7
 
1.4%
9dnmqtfrlq 6
 
1.2%
9dnppnbm69 5
 
1.0%
9dnbzcxjpa 5
 
1.0%
9dncnkmyws 5
 
1.0%
9dntlbgu91 5
 
1.0%
9dm4n3vceb 4
 
0.8%
9dnm5xb7cv 4
 
0.8%
9dnp9rfeem 4
 
0.8%
9dnofyssd6 4
 
0.8%
Other values (226) 451
90.2%
2023-12-12T17:56:19.890329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 563
 
11.3%
d 550
 
11.0%
n 441
 
8.8%
m 201
 
4.0%
l 97
 
1.9%
p 95
 
1.9%
L 87
 
1.7%
M 82
 
1.6%
A 82
 
1.6%
D 78
 
1.6%
Other values (52) 2724
54.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2520
50.4%
Uppercase Letter 1438
28.8%
Decimal Number 1042
20.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 550
21.8%
n 441
17.5%
m 201
 
8.0%
l 97
 
3.8%
p 95
 
3.8%
z 74
 
2.9%
y 69
 
2.7%
f 68
 
2.7%
v 68
 
2.7%
x 64
 
2.5%
Other values (16) 793
31.5%
Uppercase Letter
ValueCountFrequency (%)
L 87
 
6.1%
M 82
 
5.7%
A 82
 
5.7%
D 78
 
5.4%
C 77
 
5.4%
K 70
 
4.9%
H 63
 
4.4%
B 63
 
4.4%
O 60
 
4.2%
Q 57
 
4.0%
Other values (16) 719
50.0%
Decimal Number
ValueCountFrequency (%)
9 563
54.0%
0 66
 
6.3%
3 64
 
6.1%
4 60
 
5.8%
6 60
 
5.8%
1 55
 
5.3%
7 53
 
5.1%
2 45
 
4.3%
8 44
 
4.2%
5 32
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 3958
79.2%
Common 1042
 
20.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 550
 
13.9%
n 441
 
11.1%
m 201
 
5.1%
l 97
 
2.5%
p 95
 
2.4%
L 87
 
2.2%
M 82
 
2.1%
A 82
 
2.1%
D 78
 
2.0%
C 77
 
1.9%
Other values (42) 2168
54.8%
Common
ValueCountFrequency (%)
9 563
54.0%
0 66
 
6.3%
3 64
 
6.1%
4 60
 
5.8%
6 60
 
5.8%
1 55
 
5.3%
7 53
 
5.1%
2 45
 
4.3%
8 44
 
4.2%
5 32
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 563
 
11.3%
d 550
 
11.0%
n 441
 
8.8%
m 201
 
4.0%
l 97
 
1.9%
p 95
 
1.9%
L 87
 
1.7%
M 82
 
1.6%
A 82
 
1.6%
D 78
 
1.6%
Other values (52) 2724
54.5%

이력일련번호
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.864
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T17:56:20.011788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile4
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0215631
Coefficient of variation (CV)0.54804888
Kurtosis1.9404608
Mean1.864
Median Absolute Deviation (MAD)1
Skewness1.2715308
Sum932
Variance1.0435912
MonotonicityNot monotonic
2023-12-12T17:56:20.140492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 236
47.2%
2 140
28.0%
3 94
 
18.8%
4 20
 
4.0%
5 7
 
1.4%
6 2
 
0.4%
7 1
 
0.2%
ValueCountFrequency (%)
1 236
47.2%
2 140
28.0%
3 94
 
18.8%
4 20
 
4.0%
5 7
 
1.4%
6 2
 
0.4%
7 1
 
0.2%
ValueCountFrequency (%)
7 1
 
0.2%
6 2
 
0.4%
5 7
 
1.4%
4 20
 
4.0%
3 94
 
18.8%
2 140
28.0%
1 236
47.2%

이관추천구분코드
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2
342 
3
131 
1
 
27

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 342
68.4%
3 131
 
26.2%
1 27
 
5.4%

Length

2023-12-12T17:56:20.271223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:56:20.376902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 342
68.4%
3 131
 
26.2%
1 27
 
5.4%

추천부점코드
Text

Distinct74
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T17:56:20.689496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1500
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)1.2%

Sample

1st rowQAC
2nd rowTPP
3rd rowTPP
4th rowTPA
5th rowTPA
ValueCountFrequency (%)
thy 27
 
5.4%
tap 23
 
4.6%
taw 22
 
4.4%
tha 20
 
4.0%
tal 19
 
3.8%
tpa 16
 
3.2%
thi 15
 
3.0%
tbd 13
 
2.6%
qac 13
 
2.6%
tah 13
 
2.6%
Other values (64) 319
63.8%
2023-12-12T17:56:21.155927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 441
29.4%
A 229
15.3%
H 157
 
10.5%
I 92
 
6.1%
P 80
 
5.3%
J 56
 
3.7%
B 56
 
3.7%
Q 50
 
3.3%
L 39
 
2.6%
O 39
 
2.6%
Other values (15) 261
17.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1500
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 441
29.4%
A 229
15.3%
H 157
 
10.5%
I 92
 
6.1%
P 80
 
5.3%
J 56
 
3.7%
B 56
 
3.7%
Q 50
 
3.3%
L 39
 
2.6%
O 39
 
2.6%
Other values (15) 261
17.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 1500
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 441
29.4%
A 229
15.3%
H 157
 
10.5%
I 92
 
6.1%
P 80
 
5.3%
J 56
 
3.7%
B 56
 
3.7%
Q 50
 
3.3%
L 39
 
2.6%
O 39
 
2.6%
Other values (15) 261
17.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 441
29.4%
A 229
15.3%
H 157
 
10.5%
I 92
 
6.1%
P 80
 
5.3%
J 56
 
3.7%
B 56
 
3.7%
Q 50
 
3.3%
L 39
 
2.6%
O 39
 
2.6%
Other values (15) 261
17.4%

상담추천이첩결과코드
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
365 
133 
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 365
73.0%
133
 
26.6%
2 2
 
0.4%

Length

2023-12-12T17:56:21.321907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:56:21.443107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 365
99.5%
2 2
 
0.5%

추천일자
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

2023-12-12T17:56:21.606445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:56:21.735988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00.0 500
100.0%

추천결과최초입력일자
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
00:00.0
367 
0001-01-01 00:00:00.000000
133 

Length

Max length26
Median length7
Mean length12.054
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0001-01-01 00:00:00.000000
2nd row00:00.0
3rd row00:00.0
4th row00:00.0
5th row00:00.0

Common Values

ValueCountFrequency (%)
00:00.0 367
73.4%
0001-01-01 00:00:00.000000 133
 
26.6%

Length

2023-12-12T17:56:21.903421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:56:22.050282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00.0 367
58.0%
0001-01-01 133
 
21.0%
00:00:00.000000 133
 
21.0%

추천결과최초처리자직원번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct64
Distinct (%)17.4%
Missing133
Missing (%)26.6%
Infinite0
Infinite (%)0.0%
Mean3561.4005
Minimum2421
Maximum5895
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T17:56:22.249578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2421
5-th percentile2477
Q12758
median3199
Q34297.5
95-th percentile5238
Maximum5895
Range3474
Interquartile range (IQR)1539.5

Descriptive statistics

Standard deviation921.56581
Coefficient of variation (CV)0.258765
Kurtosis-0.83614774
Mean3561.4005
Median Absolute Deviation (MAD)722
Skewness0.50174295
Sum1307034
Variance849283.54
MonotonicityNot monotonic
2023-12-12T17:56:22.446082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2522 30
 
6.0%
4109 26
 
5.2%
3026 18
 
3.6%
2851 13
 
2.6%
4423 12
 
2.4%
2941 11
 
2.2%
4530 10
 
2.0%
4469 10
 
2.0%
3420 9
 
1.8%
2421 9
 
1.8%
Other values (54) 219
43.8%
(Missing) 133
26.6%
ValueCountFrequency (%)
2421 9
 
1.8%
2475 5
 
1.0%
2477 7
 
1.4%
2500 7
 
1.4%
2506 4
 
0.8%
2522 30
6.0%
2571 1
 
0.2%
2614 6
 
1.2%
2637 5
 
1.0%
2653 2
 
0.4%
ValueCountFrequency (%)
5895 1
 
0.2%
5734 2
 
0.4%
5717 2
 
0.4%
5675 3
0.6%
5633 2
 
0.4%
5579 3
0.6%
5388 5
1.0%
5238 4
0.8%
5137 6
1.2%
4928 2
 
0.4%

상담추천이첩반려사유코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
498 
99
 
2

Length

Max length2
Median length1
Mean length1.004
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
498
99.6%
99 2
 
0.4%

Length

2023-12-12T17:56:22.616680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:56:22.735377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
99 2
100.0%

추천반려사유내용
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
<NA>
264 
234 
고객사정으로 신속한 보증진행을 사유로 이첩영업점 직접 취급예정
 
2

Length

Max length34
Median length4
Mean length2.716
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
<NA> 264
52.8%
234
46.8%
고객사정으로 신속한 보증진행을 사유로 이첩영업점 직접 취급예정 2
 
0.4%

Length

2023-12-12T17:56:22.887294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:56:23.040390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 264
95.0%
고객사정으로 2
 
0.7%
신속한 2
 
0.7%
보증진행을 2
 
0.7%
사유로 2
 
0.7%
이첩영업점 2
 
0.7%
직접 2
 
0.7%
취급예정 2
 
0.7%

정책보증센터담당자직원번호
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing500
Missing (%)100.0%
Memory size4.5 KiB

현장조사예정일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0001-01-01 00:00:00.000000
500 

Length

Max length26
Median length26
Mean length26
Min length26

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0001-01-01 00:00:00.000000
2nd row0001-01-01 00:00:00.000000
3rd row0001-01-01 00:00:00.000000
4th row0001-01-01 00:00:00.000000
5th row0001-01-01 00:00:00.000000

Common Values

ValueCountFrequency (%)
0001-01-01 00:00:00.000000 500
100.0%

Length

2023-12-12T17:56:23.153726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:56:23.269199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0001-01-01 500
50.0%
00:00:00.000000 500
50.0%

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

2023-12-12T17:56:23.394586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:56:23.508729image/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

2023-12-12T17:56:23.632729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:56:23.749792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00.0 500
100.0%

최종수정수
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.948
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T17:56:23.837100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile4
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.9976405
Coefficient of variation (CV)0.51213578
Kurtosis1.9936261
Mean1.948
Median Absolute Deviation (MAD)1
Skewness1.1865333
Sum974
Variance0.99528657
MonotonicityNot monotonic
2023-12-12T17:56:23.997899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 198
39.6%
2 175
35.0%
3 96
19.2%
4 21
 
4.2%
5 7
 
1.4%
6 2
 
0.4%
7 1
 
0.2%
ValueCountFrequency (%)
1 198
39.6%
2 175
35.0%
3 96
19.2%
4 21
 
4.2%
5 7
 
1.4%
6 2
 
0.4%
7 1
 
0.2%
ValueCountFrequency (%)
7 1
 
0.2%
6 2
 
0.4%
5 7
 
1.4%
4 21
 
4.2%
3 96
19.2%
2 175
35.0%
1 198
39.6%

처리시각
Text

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T17:56:24.468104image/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

Unique500 ?
Unique (%)100.0%

Sample

1st row05:55.0
2nd row00:29.9
3rd row00:08.1
4th row54:25.1
5th row54:07.1
ValueCountFrequency (%)
05:55.0 1
 
0.2%
15:59.7 1
 
0.2%
59:54.5 1
 
0.2%
09:39.6 1
 
0.2%
12:17.5 1
 
0.2%
27:18.5 1
 
0.2%
27:36.0 1
 
0.2%
28:07.1 1
 
0.2%
28:10.5 1
 
0.2%
28:47.1 1
 
0.2%
Other values (490) 490
98.0%
2023-12-12T17:56:25.432864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
5 331
9.5%
3 323
9.2%
0 320
9.1%
4 315
9.0%
1 313
8.9%
2 304
8.7%
7 156
 
4.5%
9 151
 
4.3%
Other values (2) 287
8.2%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 331
13.2%
3 323
12.9%
0 320
12.8%
4 315
12.6%
1 313
12.5%
2 304
12.2%
7 156
6.2%
9 151
6.0%
8 144
5.8%
6 143
5.7%
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%
5 331
9.5%
3 323
9.2%
0 320
9.1%
4 315
9.0%
1 313
8.9%
2 304
8.7%
7 156
 
4.5%
9 151
 
4.3%
Other values (2) 287
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
5 331
9.5%
3 323
9.2%
0 320
9.1%
4 315
9.0%
1 313
8.9%
2 304
8.7%
7 156
 
4.5%
9 151
 
4.3%
Other values (2) 287
8.2%

처리직원번호
Real number (ℝ)

HIGH CORRELATION 

Distinct144
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3757.688
Minimum1906
Maximum6096
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T17:56:25.643778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1906
5-th percentile2477
Q13033
median3984
Q34149
95-th percentile5238
Maximum6096
Range4190
Interquartile range (IQR)1116

Descriptive statistics

Standard deviation813.97645
Coefficient of variation (CV)0.21661629
Kurtosis-0.15168218
Mean3757.688
Median Absolute Deviation (MAD)485
Skewness0.075959898
Sum1878844
Variance662557.66
MonotonicityNot monotonic
2023-12-12T17:56:25.816299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4109 29
 
5.8%
4043 27
 
5.4%
4129 25
 
5.0%
2522 22
 
4.4%
3432 16
 
3.2%
3026 14
 
2.8%
4423 12
 
2.4%
4469 10
 
2.0%
3420 9
 
1.8%
2421 9
 
1.8%
Other values (134) 327
65.4%
ValueCountFrequency (%)
1906 4
 
0.8%
2177 5
 
1.0%
2421 9
1.8%
2458 1
 
0.2%
2475 3
 
0.6%
2477 4
 
0.8%
2500 4
 
0.8%
2506 3
 
0.6%
2522 22
4.4%
2571 1
 
0.2%
ValueCountFrequency (%)
6096 1
 
0.2%
5928 1
 
0.2%
5895 1
 
0.2%
5759 1
 
0.2%
5734 2
0.4%
5717 2
0.4%
5675 3
0.6%
5633 2
0.4%
5579 3
0.6%
5399 1
 
0.2%

최초처리시각
Text

Distinct236
Distinct (%)47.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T17:56:26.208852image/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

Unique73 ?
Unique (%)14.6%

Sample

1st row05:55.0
2nd row39:16.9
3rd row39:16.9
4th row29:14.8
5th row29:14.8
ValueCountFrequency (%)
06:26.3 7
 
1.4%
30:45.7 6
 
1.2%
48:43.9 5
 
1.0%
16:57.6 5
 
1.0%
12:58.3 5
 
1.0%
07:19.0 5
 
1.0%
28:07.1 4
 
0.8%
22:07.4 4
 
0.8%
01:45.3 4
 
0.8%
17:17.3 4
 
0.8%
Other values (226) 451
90.2%
2023-12-12T17:56:26.773739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
4 337
9.6%
0 329
9.4%
1 320
9.1%
3 316
9.0%
2 295
8.4%
5 278
7.9%
7 179
 
5.1%
6 162
 
4.6%
Other values (2) 284
8.1%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 337
13.5%
0 329
13.2%
1 320
12.8%
3 316
12.6%
2 295
11.8%
5 278
11.1%
7 179
7.2%
6 162
6.5%
8 145
5.8%
9 139
5.6%
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%
4 337
9.6%
0 329
9.4%
1 320
9.1%
3 316
9.0%
2 295
8.4%
5 278
7.9%
7 179
 
5.1%
6 162
 
4.6%
Other values (2) 284
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
4 337
9.6%
0 329
9.4%
1 320
9.1%
3 316
9.0%
2 295
8.4%
5 278
7.9%
7 179
 
5.1%
6 162
 
4.6%
Other values (2) 284
8.1%

최초처리직원번호
Real number (ℝ)

HIGH CORRELATION 

Distinct128
Distinct (%)25.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3982.582
Minimum2458
Maximum5928
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T17:56:26.985258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2458
5-th percentile3082
Q13575
median3984
Q34218
95-th percentile5388
Maximum5928
Range3470
Interquartile range (IQR)643

Descriptive statistics

Standard deviation609.62239
Coefficient of variation (CV)0.15307215
Kurtosis1.5275742
Mean3982.582
Median Absolute Deviation (MAD)335
Skewness0.81762306
Sum1991291
Variance371639.45
MonotonicityNot monotonic
2023-12-12T17:56:27.207995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3413 21
 
4.2%
4043 15
 
3.0%
4423 12
 
2.4%
4109 10
 
2.0%
3644 10
 
2.0%
3420 9
 
1.8%
4303 9
 
1.8%
3474 9
 
1.8%
4056 8
 
1.6%
4123 8
 
1.6%
Other values (118) 389
77.8%
ValueCountFrequency (%)
2458 2
 
0.4%
2522 5
1.0%
2962 7
1.4%
3055 5
1.0%
3060 2
 
0.4%
3082 5
1.0%
3083 7
1.4%
3272 3
0.6%
3279 3
0.6%
3280 2
 
0.4%
ValueCountFrequency (%)
5928 5
1.0%
5895 1
 
0.2%
5734 2
 
0.4%
5717 2
 
0.4%
5675 3
0.6%
5633 2
 
0.4%
5579 3
0.6%
5467 1
 
0.2%
5399 3
0.6%
5388 5
1.0%

Interactions

2023-12-12T17:56:18.178737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:15.685167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:16.353913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:17.027602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:17.678501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:18.291468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:15.836278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:16.511089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:17.169475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:17.781414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:18.409067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:15.968089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:16.643765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:17.297263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:17.897930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:18.508519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:16.096301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:16.761250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:17.439140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:18.002363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:18.605866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:16.216847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:16.884866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:17.561106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:56:18.084617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:56:27.379980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이력일련번호이관추천구분코드추천부점코드상담추천이첩결과코드추천결과최초입력일자추천결과최초처리자직원번호상담추천이첩반려사유코드추천반려사유내용최종수정수처리직원번호최초처리직원번호
이력일련번호1.0000.1080.0000.5330.5610.2180.0000.0000.9230.4160.000
이관추천구분코드0.1081.0000.9590.5700.2160.9010.0000.0000.1280.5400.522
추천부점코드0.0000.9591.0000.6080.2830.9780.5780.7540.0000.9080.949
상담추천이첩결과코드0.5330.5700.6081.0001.0000.0351.0000.9220.6220.4980.228
추천결과최초입력일자0.5610.2160.2831.0001.000NaN0.000NaN0.6850.5710.243
추천결과최초처리자직원번호0.2180.9010.9780.035NaN1.0000.0350.1950.3040.8850.828
상담추천이첩반려사유코드0.0000.0000.5781.0000.0000.0351.0000.9220.0000.2750.097
추천반려사유내용0.0000.0000.7540.922NaN0.1950.9221.0000.0000.8750.309
최종수정수0.9230.1280.0000.6220.6850.3040.0000.0001.0000.4730.000
처리직원번호0.4160.5400.9080.4980.5710.8850.2750.8750.4731.0000.913
최초처리직원번호0.0000.5220.9490.2280.2430.8280.0970.3090.0000.9131.000
2023-12-12T17:56:27.572702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상담추천이첩결과코드이관추천구분코드상담추천이첩반려사유코드추천결과최초입력일자추천반려사유내용
상담추천이첩결과코드1.0000.2490.9990.9990.747
이관추천구분코드0.2491.0000.0000.3540.000
상담추천이첩반려사유코드0.9990.0001.0000.0000.747
추천결과최초입력일자0.9990.3540.0001.0001.000
추천반려사유내용0.7470.0000.7471.0001.000
2023-12-12T17:56:27.722687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이력일련번호추천결과최초처리자직원번호최종수정수처리직원번호최초처리직원번호이관추천구분코드상담추천이첩결과코드추천결과최초입력일자상담추천이첩반려사유코드추천반려사유내용
이력일련번호1.000-0.002-0.091-0.109-0.0470.0720.4190.6000.0000.000
추천결과최초처리자직원번호-0.0021.000-0.2650.8790.5500.6350.0431.0000.0430.209
최종수정수-0.091-0.2651.000-0.101-0.0610.0860.5160.7360.0000.000
처리직원번호-0.1090.879-0.1011.0000.5980.3830.3450.4400.2100.689
최초처리직원번호-0.0470.550-0.0610.5981.0000.3660.1380.1830.0740.233
이관추천구분코드0.0720.6350.0860.3830.3661.0000.2490.3540.0000.000
상담추천이첩결과코드0.4190.0430.5160.3450.1380.2491.0000.9990.9990.747
추천결과최초입력일자0.6001.0000.7360.4400.1830.3540.9991.0000.0001.000
상담추천이첩반려사유코드0.0000.0430.0000.2100.0740.0000.9990.0001.0000.747
추천반려사유내용0.0000.2090.0000.6890.2330.0000.7471.0000.7471.000

Missing values

2023-12-12T17:56:18.786044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:56:19.093215image/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

상담ID이력일련번호이관추천구분코드추천부점코드상담추천이첩결과코드추천일자추천결과최초입력일자추천결과최초처리자직원번호상담추천이첩반려사유코드추천반려사유내용정책보증센터담당자직원번호현장조사예정일자유효개시일자유효종료일자최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
09dnSU0Epzz11QAC00:00.00001-01-01 00:00:00.000000<NA><NA><NA>0001-01-01 00:00:00.00000000:00.000:00.0105:55.0408305:55.04083
19dnDMAMEiW12TPP100:00.000:00.04109<NA>0001-01-01 00:00:00.00000000:00.000:00.0300:29.9410939:16.95399
29dnDMAMEiW32TPP100:00.000:00.04109<NA>0001-01-01 00:00:00.00000000:00.000:00.0200:08.1410939:16.95399
39dnLmKBv7a12TPA100:00.000:00.04109<NA>0001-01-01 00:00:00.00000000:00.000:00.0354:25.1410929:14.84218
49dnLmKBv7a32TPA100:00.000:00.04109<NA>0001-01-01 00:00:00.00000000:00.000:00.0254:07.1410929:14.84218
59dnBZCxJpA12THU100:00.000:00.03026<NA>0001-01-01 00:00:00.00000000:00.000:00.0533:58.0302616:57.63082
69dnCrTrU8q12TAH100:00.000:00.03033<NA>0001-01-01 00:00:00.00000000:00.000:00.0321:57.2404344:04.63272
79dnLcvTzLL12THI100:00.000:00.02881<NA>0001-01-01 00:00:00.00000000:00.000:00.0319:13.1404341:54.54056
89dnCnkmyWS13TAH100:00.000:00.04123<NA><NA>0001-01-01 00:00:00.00000000:00.000:00.0519:00.9412312:58.34123
99dnCrTrU8q32TAH100:00.000:00.03033<NA>0001-01-01 00:00:00.00000000:00.000:00.0218:56.4303344:04.63272
상담ID이력일련번호이관추천구분코드추천부점코드상담추천이첩결과코드추천일자추천결과최초입력일자추천결과최초처리자직원번호상담추천이첩반려사유코드추천반려사유내용정책보증센터담당자직원번호현장조사예정일자유효개시일자유효종료일자최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
4909dnlEaQip913JBA100:00.000:00.04303<NA><NA>0001-01-01 00:00:00.00000000:00.000:00.0110:57.9430310:57.94303
4919dm4XLjvkL13JBA100:00.000:00.04303<NA><NA>0001-01-01 00:00:00.00000000:00.000:00.0110:20.7430310:20.74303
4929dnftv9PbI13JBA100:00.000:00.04303<NA><NA>0001-01-01 00:00:00.00000000:00.000:00.0108:13.4430308:13.44303
4939dmw9b3Yy913JBA100:00.000:00.04303<NA><NA>0001-01-01 00:00:00.00000000:00.000:00.0107:36.9430307:36.94303
4949dmw2PYMzX13JBA100:00.000:00.04303<NA><NA>0001-01-01 00:00:00.00000000:00.000:00.0106:54.6430306:54.64303
4959dlYk8j9Lr13JBA100:00.000:00.04303<NA><NA>0001-01-01 00:00:00.00000000:00.000:00.0106:24.5430306:24.54303
4969dlQsqC6Ym13JBA100:00.000:00.04303<NA><NA>0001-01-01 00:00:00.00000000:00.000:00.0105:48.9430305:48.94303
4979dlYnCSx9113JBA100:00.000:00.04303<NA><NA>0001-01-01 00:00:00.00000000:00.000:00.0105:10.0430305:10.04303
4989dlYidiLp713JBA100:00.000:00.04303<NA><NA>0001-01-01 00:00:00.00000000:00.000:00.0102:48.2430302:48.24303
4999dnljRQAR722THY00:00.00001-01-01 00:00:00.000000<NA><NA><NA>0001-01-01 00:00:00.00000000:00.000:00.0136:09.8396136:09.83961