Overview

Dataset statistics

Number of variables37
Number of observations500
Missing cells189
Missing cells (%)1.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory151.0 KiB
Average record size in memory309.3 B

Variable types

Text6
Categorical23
DateTime1
Numeric6
Boolean1

Dataset

Description해당 파일 데이터는 신용보증기금의 보증상담마스터에 대한 정보를 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15092833/fileData.do

Alerts

업무구분코드 has constant value ""Constant
상담일자 has constant value ""Constant
상담채널구분코드 has constant value ""Constant
직위재구분코드 has constant value ""Constant
상담심의여부 has constant value ""Constant
상담변경사유코드 has constant value ""Constant
신청기한월수 has constant value ""Constant
한도방법건별발급기한 has constant value ""Constant
상담일련번호 is highly imbalanced (76.0%)Imbalance
일반특별구분코드 is highly imbalanced (69.6%)Imbalance
건별구분코드 is highly imbalanced (50.1%)Imbalance
취급종류코드 is highly imbalanced (65.8%)Imbalance
미확정신청기한명 is highly imbalanced (57.3%)Imbalance
상담결과구분코드 is highly imbalanced (53.9%)Imbalance
보증신청처리상태코드 is highly imbalanced (87.9%)Imbalance
책임종료일자 is highly imbalanced (91.9%)Imbalance
삭제여부 is highly imbalanced (70.5%)Imbalance
상담직원번호 has 189 (37.8%) missing valuesMissing
상담ID has unique valuesUnique
상담금액 has 47 (9.4%) zerosZeros
보증비율 has 277 (55.4%) zerosZeros

Reproduction

Analysis started2023-12-12 16:35:14.522509
Analysis finished2023-12-12 16:35:14.966726
Duration0.44 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

상담ID
Text

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T01:35:15.121199image/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

Unique500 ?
Unique (%)100.0%

Sample

1st row9dnSXLsKjL
2nd row9dnSZ3cc0s
3rd row9dnS0fbweI
4th row9dnSUvJ26O
5th row9dnSYCLyKU
ValueCountFrequency (%)
9dnsxlskjl 1
 
0.2%
9dnodhoedd 1
 
0.2%
9dnopqne70 1
 
0.2%
9dnobauono 1
 
0.2%
9dnowdurpd 1
 
0.2%
9dnoxzx9df 1
 
0.2%
9dnozyc1or 1
 
0.2%
9dnobr1qle 1
 
0.2%
9dnobvijc2 1
 
0.2%
9dnoaof0ws 1
 
0.2%
Other values (490) 490
98.0%
2023-12-13T01:35:15.422115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 545
 
10.9%
d 542
 
10.8%
n 528
 
10.6%
S 284
 
5.7%
O 245
 
4.9%
J 76
 
1.5%
L 72
 
1.4%
N 71
 
1.4%
K 68
 
1.4%
M 65
 
1.3%
Other values (52) 2504
50.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2235
44.7%
Uppercase Letter 1862
37.2%
Decimal Number 903
18.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 542
24.3%
n 528
23.6%
z 65
 
2.9%
u 61
 
2.7%
v 58
 
2.6%
s 57
 
2.6%
l 55
 
2.5%
o 53
 
2.4%
k 52
 
2.3%
t 52
 
2.3%
Other values (16) 712
31.9%
Uppercase Letter
ValueCountFrequency (%)
S 284
 
15.3%
O 245
 
13.2%
J 76
 
4.1%
L 72
 
3.9%
N 71
 
3.8%
K 68
 
3.7%
M 65
 
3.5%
F 63
 
3.4%
W 62
 
3.3%
Y 59
 
3.2%
Other values (16) 797
42.8%
Decimal Number
ValueCountFrequency (%)
9 545
60.4%
0 46
 
5.1%
1 45
 
5.0%
4 44
 
4.9%
5 43
 
4.8%
7 43
 
4.8%
3 37
 
4.1%
8 36
 
4.0%
6 34
 
3.8%
2 30
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 4097
81.9%
Common 903
 
18.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 542
 
13.2%
n 528
 
12.9%
S 284
 
6.9%
O 245
 
6.0%
J 76
 
1.9%
L 72
 
1.8%
N 71
 
1.7%
K 68
 
1.7%
M 65
 
1.6%
z 65
 
1.6%
Other values (42) 2081
50.8%
Common
ValueCountFrequency (%)
9 545
60.4%
0 46
 
5.1%
1 45
 
5.0%
4 44
 
4.9%
5 43
 
4.8%
7 43
 
4.8%
3 37
 
4.1%
8 36
 
4.0%
6 34
 
3.8%
2 30
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 545
 
10.9%
d 542
 
10.8%
n 528
 
10.6%
S 284
 
5.7%
O 245
 
4.9%
J 76
 
1.5%
L 72
 
1.4%
N 71
 
1.4%
K 68
 
1.4%
M 65
 
1.3%
Other values (52) 2504
50.1%

업무구분코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
G
500 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
G 500
100.0%

Length

2023-12-13T01:35:15.543668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:35:15.635365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
g 500
100.0%

상담일자
Date

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2023-12-13 00:00:00
Maximum2023-12-13 00:00:00
2023-12-13T01:35:15.696720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:35:15.764797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

상담일련번호
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
466 
2
 
32
3
 
2

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 (%)
1 466
93.2%
2 32
 
6.4%
3 2
 
0.4%

Length

2023-12-13T01:35:15.855900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:35:15.944969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 466
93.2%
2 32
 
6.4%
3 2
 
0.4%
Distinct485
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T01:35:16.131614image/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

Unique472 ?
Unique (%)94.4%

Sample

1st row9dnSXLrSVb
2nd row9dnSZ3a2IR
3rd row9dnS0e9VvK
4th row9dnSUvI2yT
5th row9dnSYCKgh0
ValueCountFrequency (%)
9dnspspt5w 3
 
0.6%
9dnovjzy0a 3
 
0.6%
9dnotjpufh 2
 
0.4%
9dnsfpmik0 2
 
0.4%
9dnsihdesb 2
 
0.4%
9dnslla9tu 2
 
0.4%
9dnslwvqwa 2
 
0.4%
9dnoolykvy 2
 
0.4%
9dnozfrlls 2
 
0.4%
9dnsppyema 2
 
0.4%
Other values (475) 478
95.6%
2023-12-13T01:35:16.425383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 548
 
11.0%
9 539
 
10.8%
n 539
 
10.8%
S 296
 
5.9%
O 228
 
4.6%
z 78
 
1.6%
N 75
 
1.5%
L 70
 
1.4%
K 70
 
1.4%
J 67
 
1.3%
Other values (52) 2490
49.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2257
45.1%
Uppercase Letter 1844
36.9%
Decimal Number 899
 
18.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 548
24.3%
n 539
23.9%
z 78
 
3.5%
p 60
 
2.7%
m 57
 
2.5%
u 57
 
2.5%
o 56
 
2.5%
q 54
 
2.4%
v 52
 
2.3%
l 52
 
2.3%
Other values (16) 704
31.2%
Uppercase Letter
ValueCountFrequency (%)
S 296
 
16.1%
O 228
 
12.4%
N 75
 
4.1%
L 70
 
3.8%
K 70
 
3.8%
J 67
 
3.6%
Z 64
 
3.5%
P 61
 
3.3%
W 59
 
3.2%
G 57
 
3.1%
Other values (16) 797
43.2%
Decimal Number
ValueCountFrequency (%)
9 539
60.0%
4 49
 
5.5%
0 49
 
5.5%
1 43
 
4.8%
5 42
 
4.7%
3 40
 
4.4%
8 37
 
4.1%
2 36
 
4.0%
7 35
 
3.9%
6 29
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 4101
82.0%
Common 899
 
18.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 548
 
13.4%
n 539
 
13.1%
S 296
 
7.2%
O 228
 
5.6%
z 78
 
1.9%
N 75
 
1.8%
L 70
 
1.7%
K 70
 
1.7%
J 67
 
1.6%
Z 64
 
1.6%
Other values (42) 2066
50.4%
Common
ValueCountFrequency (%)
9 539
60.0%
4 49
 
5.5%
0 49
 
5.5%
1 43
 
4.8%
5 42
 
4.7%
3 40
 
4.4%
8 37
 
4.1%
2 36
 
4.0%
7 35
 
3.9%
6 29
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 548
 
11.0%
9 539
 
10.8%
n 539
 
10.8%
S 296
 
5.9%
O 228
 
4.6%
z 78
 
1.6%
N 75
 
1.5%
L 70
 
1.4%
K 70
 
1.4%
J 67
 
1.3%
Other values (52) 2490
49.8%

상담채널구분코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
500 

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 (%)
1 500
100.0%

Length

2023-12-13T01:35:16.529558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:35:16.605571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 500
100.0%

일반특별구분코드
Categorical

IMBALANCE 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
434 
47 
2
 
16
6
 
2
7
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
1 434
86.8%
47
 
9.4%
2 16
 
3.2%
6 2
 
0.4%
7 1
 
0.2%

Length

2023-12-13T01:35:16.690090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:35:16.773638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 434
95.8%
2 16
 
3.5%
6 2
 
0.4%
7 1
 
0.2%

직위재구분코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
500 

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 (%)
1 500
100.0%

Length

2023-12-13T01:35:16.859000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:35:16.932297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 500
100.0%

상담금액
Real number (ℝ)

ZEROS 

Distinct144
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8869269 × 108
Minimum0
Maximum5.6 × 109
Zeros47
Zeros (%)9.4%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T01:35:17.012804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q190000000
median1.63625 × 108
Q33 × 108
95-th percentile1 × 109
Maximum5.6 × 109
Range5.6 × 109
Interquartile range (IQR)2.1 × 108

Descriptive statistics

Standard deviation4.6306243 × 108
Coefficient of variation (CV)1.6039978
Kurtosis41.586246
Mean2.8869269 × 108
Median Absolute Deviation (MAD)1.21375 × 108
Skewness5.1796776
Sum1.4434634 × 1011
Variance2.1442681 × 1017
MonotonicityNot monotonic
2023-12-13T01:35:17.117193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000000 56
 
11.2%
300000000 49
 
9.8%
0 47
 
9.4%
200000000 27
 
5.4%
285000000 19
 
3.8%
90000000 14
 
2.8%
95000000 12
 
2.4%
500000000 12
 
2.4%
1000000000 11
 
2.2%
99000000 11
 
2.2%
Other values (134) 242
48.4%
ValueCountFrequency (%)
0 47
9.4%
5000000 1
 
0.2%
5600000 1
 
0.2%
7200000 1
 
0.2%
9690000 1
 
0.2%
11700000 1
 
0.2%
14365000 1
 
0.2%
15000000 1
 
0.2%
15300000 1
 
0.2%
17600000 1
 
0.2%
ValueCountFrequency (%)
5600000000 1
 
0.2%
3000000000 1
 
0.2%
2970000000 1
 
0.2%
2500000000 1
 
0.2%
2100000000 1
 
0.2%
2000000000 4
0.8%
1995000000 1
 
0.2%
1800000000 2
0.4%
1600000000 1
 
0.2%
1550000000 1
 
0.2%

거래방법코드
Real number (ℝ)

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.914
Minimum1
Maximum62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T01:35:17.205587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum62
Range61
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.9556312
Coefficient of variation (CV)3.1116151
Kurtosis73.701324
Mean1.914
Median Absolute Deviation (MAD)0
Skewness8.2919619
Sum957
Variance35.469543
MonotonicityNot monotonic
2023-12-13T01:35:17.291530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 464
92.8%
2 14
 
2.8%
4 10
 
2.0%
21 7
 
1.4%
62 3
 
0.6%
46 2
 
0.4%
ValueCountFrequency (%)
1 464
92.8%
2 14
 
2.8%
4 10
 
2.0%
21 7
 
1.4%
46 2
 
0.4%
62 3
 
0.6%
ValueCountFrequency (%)
62 3
 
0.6%
46 2
 
0.4%
21 7
 
1.4%
4 10
 
2.0%
2 14
 
2.8%
1 464
92.8%

건별구분코드
Categorical

IMBALANCE 

Distinct8
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
327 
3
102 
47 
7
 
8
21
 
8
Other values (3)
 
8

Length

Max length2
Median length1
Mean length1.016
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
1 327
65.4%
3 102
 
20.4%
47
 
9.4%
7 8
 
1.6%
21 8
 
1.6%
4 4
 
0.8%
2 3
 
0.6%
5 1
 
0.2%

Length

2023-12-13T01:35:17.390586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:35:17.486928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 327
72.2%
3 102
 
22.5%
7 8
 
1.8%
21 8
 
1.8%
4 4
 
0.9%
2 3
 
0.7%
5 1
 
0.2%

취급종류코드
Categorical

IMBALANCE 

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
411 
55 
42
 
15
10
 
14
5
 
3
Other values (2)
 
2

Length

Max length2
Median length1
Mean length1.058
Min length1

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st row1
2nd row10
3rd row
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 411
82.2%
55
 
11.0%
42 15
 
3.0%
10 14
 
2.8%
5 3
 
0.6%
6 1
 
0.2%
2 1
 
0.2%

Length

2023-12-13T01:35:17.603028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:35:17.708844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 411
92.4%
42 15
 
3.4%
10 14
 
3.1%
5 3
 
0.7%
6 1
 
0.2%
2 1
 
0.2%
Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
274 
1
190 
2
36 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
274
54.8%
1 190
38.0%
2 36
 
7.2%

Length

2023-12-13T01:35:17.851010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:35:17.952327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 190
84.1%
2 36
 
15.9%
Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
230 
2
108 
3
97 
55 
99
 
10

Length

Max length2
Median length1
Mean length1.02
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 230
46.0%
2 108
21.6%
3 97
19.4%
55
 
11.0%
99 10
 
2.0%

Length

2023-12-13T01:35:18.057947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:35:18.177469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 230
51.7%
2 108
24.3%
3 97
21.8%
99 10
 
2.2%
Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
266 
1
229 
2
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
266
53.2%
1 229
45.8%
2 5
 
1.0%

Length

2023-12-13T01:35:18.318933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:35:18.407382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 229
97.9%
2 5
 
2.1%

보증비율
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.77
Minimum0
Maximum100
Zeros277
Zeros (%)55.4%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T01:35:18.490580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q390
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)90

Descriptive statistics

Standard deviation45.714733
Coefficient of variation (CV)1.1212836
Kurtosis-1.9078113
Mean40.77
Median Absolute Deviation (MAD)0
Skewness0.24780924
Sum20385
Variance2089.8368
MonotonicityNot monotonic
2023-12-13T01:35:18.590375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 277
55.4%
100 60
 
12.0%
90 53
 
10.6%
85 45
 
9.0%
95 40
 
8.0%
80 24
 
4.8%
70 1
 
0.2%
ValueCountFrequency (%)
0 277
55.4%
70 1
 
0.2%
80 24
 
4.8%
85 45
 
9.0%
90 53
 
10.6%
95 40
 
8.0%
100 60
 
12.0%
ValueCountFrequency (%)
100 60
 
12.0%
95 40
 
8.0%
90 53
 
10.6%
85 45
 
9.0%
80 24
 
4.8%
70 1
 
0.2%
0 277
55.4%
Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
242 
2
211 
47 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 242
48.4%
2 211
42.2%
47
 
9.4%

Length

2023-12-13T01:35:18.733176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:35:18.834916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 242
53.4%
2 211
46.6%

미확정신청기한명
Categorical

IMBALANCE 

Distinct28
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
<NA>
268 
발급후1년
155 
발급후3년
 
13
발급후2년
 
9
발급후5년
 
9
Other values (23)
46 

Length

Max length36
Median length4
Mean length4.66
Min length2

Unique

Unique12 ?
Unique (%)2.4%

Sample

1st row발급후1년
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 268
53.6%
발급후1년 155
31.0%
발급후3년 13
 
2.6%
발급후2년 9
 
1.8%
발급후5년 9
 
1.8%
1년 6
 
1.2%
승인후3년 6
 
1.2%
발급후 1년 5
 
1.0%
보증서 발급일로부터 1년 3
 
0.6%
승인후1년 2
 
0.4%
Other values (18) 24
 
4.8%

Length

2023-12-13T01:35:18.946532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 268
50.8%
발급후1년 155
29.4%
1년 22
 
4.2%
발급후3년 13
 
2.5%
발급후2년 9
 
1.7%
발급후5년 9
 
1.7%
승인후3년 6
 
1.1%
발급후 6
 
1.1%
발급일로부터 6
 
1.1%
보증서 5
 
0.9%
Other values (18) 29
 
5.5%

상담결과구분코드
Categorical

IMBALANCE 

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
328 
8
161 
7
 
8
6
 
2
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row8
2nd row8
3rd row8
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 328
65.6%
8 161
32.2%
7 8
 
1.6%
6 2
 
0.4%
2 1
 
0.2%

Length

2023-12-13T01:35:19.060524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:35:19.171439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 328
65.6%
8 161
32.2%
7 8
 
1.6%
6 2
 
0.4%
2 1
 
0.2%
Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.642
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T01:35:19.646518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median12
Q312
95-th percentile12
Maximum15
Range14
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.0700342
Coefficient of variation (CV)0.58667371
Kurtosis-1.3583021
Mean8.642
Median Absolute Deviation (MAD)0
Skewness-0.76958173
Sum4321
Variance25.705246
MonotonicityNot monotonic
2023-12-13T01:35:19.757629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
12 330
66.0%
1 120
 
24.0%
2 38
 
7.6%
15 8
 
1.6%
14 2
 
0.4%
4 1
 
0.2%
13 1
 
0.2%
ValueCountFrequency (%)
1 120
 
24.0%
2 38
 
7.6%
4 1
 
0.2%
12 330
66.0%
13 1
 
0.2%
14 2
 
0.4%
15 8
 
1.6%
ValueCountFrequency (%)
15 8
 
1.6%
14 2
 
0.4%
13 1
 
0.2%
12 330
66.0%
4 1
 
0.2%
2 38
 
7.6%
1 120
 
24.0%

상담심의여부
Categorical

CONSTANT 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
500
100.0%

Length

2023-12-13T01:35:19.876671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:35:19.973929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

상담직원번호
Real number (ℝ)

MISSING 

Distinct206
Distinct (%)66.2%
Missing189
Missing (%)37.8%
Infinite0
Infinite (%)0.0%
Mean4018.7203
Minimum2458
Maximum6200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T01:35:20.074788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2458
5-th percentile2710
Q13574.5
median3982
Q34216.5
95-th percentile5661.5
Maximum6200
Range3742
Interquartile range (IQR)642

Descriptive statistics

Standard deviation774.2714
Coefficient of variation (CV)0.19266616
Kurtosis0.89699071
Mean4018.7203
Median Absolute Deviation (MAD)306
Skewness0.74807834
Sum1249822
Variance599496.2
MonotonicityNot monotonic
2023-12-13T01:35:20.218496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4055 6
 
1.2%
2710 6
 
1.2%
2522 5
 
1.0%
3745 5
 
1.0%
3535 5
 
1.0%
3740 4
 
0.8%
4238 4
 
0.8%
3746 4
 
0.8%
3723 3
 
0.6%
4115 3
 
0.6%
Other values (196) 266
53.2%
(Missing) 189
37.8%
ValueCountFrequency (%)
2458 1
 
0.2%
2475 1
 
0.2%
2500 2
 
0.4%
2522 5
1.0%
2653 1
 
0.2%
2689 1
 
0.2%
2710 6
1.2%
2756 1
 
0.2%
2851 1
 
0.2%
2881 1
 
0.2%
ValueCountFrequency (%)
6200 1
0.2%
6133 1
0.2%
6084 1
0.2%
6080 1
0.2%
6055 1
0.2%
6045 1
0.2%
6044 1
0.2%
6038 2
0.4%
6026 1
0.2%
6024 1
0.2%

상담변경사유코드
Categorical

CONSTANT 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
500
100.0%

Length

2023-12-13T01:35:20.366842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:35:20.480916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

상담팀코드
Categorical

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
189 
1
129 
2
102 
3
55 
4
 
16
Other values (2)
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row4
3rd row
4th row3
5th row2

Common Values

ValueCountFrequency (%)
189
37.8%
1 129
25.8%
2 102
20.4%
3 55
 
11.0%
4 16
 
3.2%
9 7
 
1.4%
A 2
 
0.4%

Length

2023-12-13T01:35:20.635632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:35:20.774054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 129
41.5%
2 102
32.8%
3 55
17.7%
4 16
 
5.1%
9 7
 
2.3%
a 2
 
0.6%

보증신청처리상태코드
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
483 
4
 
14
3
 
2
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row4
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 483
96.6%
4 14
 
2.8%
3 2
 
0.4%
2 1
 
0.2%

Length

2023-12-13T01:35:20.921801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:35:21.060678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 483
96.6%
4 14
 
2.8%
3 2
 
0.4%
2 1
 
0.2%
Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
273 
1
219 
2
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
273
54.6%
1 219
43.8%
2 8
 
1.6%

Length

2023-12-13T01:35:21.203033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:35:21.329587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 219
96.5%
2 8
 
3.5%
Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0
274 
1
185 
3
 
20
5
 
11
2
 
10

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 (%)
0 274
54.8%
1 185
37.0%
3 20
 
4.0%
5 11
 
2.2%
2 10
 
2.0%

Length

2023-12-13T01:35:21.473743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:35:21.613422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 274
54.8%
1 185
37.0%
3 20
 
4.0%
5 11
 
2.2%
2 10
 
2.0%

신청기한월수
Categorical

CONSTANT 

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

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 (%)
0 500
100.0%

Length

2023-12-13T01:35:21.776499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:35:21.904043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 500
100.0%
Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
291 
1
198 
2
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
291
58.2%
1 198
39.6%
2 11
 
2.2%

Length

2023-12-13T01:35:22.029316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:35:22.167533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 198
94.7%
2 11
 
5.3%

한도방법건별발급기한
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

2023-12-13T01:35:22.323510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:35:22.441864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0001-01-01 500
50.0%
00:00:00.000000 500
50.0%

책임종료일자
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0001-01-01 00:00:00.000000
495 
00:00.0
 
5

Length

Max length26
Median length26
Mean length25.81
Min length7

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 495
99.0%
00:00.0 5
 
1.0%

Length

2023-12-13T01:35:22.587714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:35:22.716712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0001-01-01 495
49.7%
00:00:00.000000 495
49.7%
00:00.0 5
 
0.5%

삭제여부
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
474 
True
 
26
ValueCountFrequency (%)
False 474
94.8%
True 26
 
5.2%
2023-12-13T01:35:22.803110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

최종수정수
Real number (ℝ)

Distinct19
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.976
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-13T01:35:22.894540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q36
95-th percentile10
Maximum21
Range20
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.8635334
Coefficient of variation (CV)0.57546893
Kurtosis4.3501982
Mean4.976
Median Absolute Deviation (MAD)2
Skewness1.6211477
Sum2488
Variance8.1998236
MonotonicityNot monotonic
2023-12-13T01:35:23.024756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
5 138
27.6%
3 130
26.0%
6 35
 
7.0%
1 33
 
6.6%
4 32
 
6.4%
7 32
 
6.4%
2 26
 
5.2%
8 21
 
4.2%
9 18
 
3.6%
10 15
 
3.0%
Other values (9) 20
 
4.0%
ValueCountFrequency (%)
1 33
 
6.6%
2 26
 
5.2%
3 130
26.0%
4 32
 
6.4%
5 138
27.6%
6 35
 
7.0%
7 32
 
6.4%
8 21
 
4.2%
9 18
 
3.6%
10 15
 
3.0%
ValueCountFrequency (%)
21 1
 
0.2%
19 1
 
0.2%
17 1
 
0.2%
16 1
 
0.2%
15 2
 
0.4%
14 4
 
0.8%
13 3
 
0.6%
12 6
 
1.2%
11 1
 
0.2%
10 15
3.0%
Distinct497
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T01:35:23.461125image/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

Unique494 ?
Unique (%)98.8%

Sample

1st row31:01.1
2nd row30:37.0
3rd row30:28.9
4th row30:25.7
5th row30:15.7
ValueCountFrequency (%)
15:04.2 2
 
0.4%
36:44.3 2
 
0.4%
46:27.0 2
 
0.4%
20:41.8 1
 
0.2%
07:48.8 1
 
0.2%
11:17.4 1
 
0.2%
12:15.8 1
 
0.2%
12:44.4 1
 
0.2%
15:12.8 1
 
0.2%
16:51.5 1
 
0.2%
Other values (487) 487
97.4%
2023-12-13T01:35:23.990435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
4 349
10.0%
2 337
9.6%
0 317
9.1%
1 308
8.8%
5 305
8.7%
3 293
8.4%
6 153
 
4.4%
9 150
 
4.3%
Other values (2) 288
8.2%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 349
14.0%
2 337
13.5%
0 317
12.7%
1 308
12.3%
5 305
12.2%
3 293
11.7%
6 153
6.1%
9 150
6.0%
8 149
6.0%
7 139
 
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%
4 349
10.0%
2 337
9.6%
0 317
9.1%
1 308
8.8%
5 305
8.7%
3 293
8.4%
6 153
 
4.4%
9 150
 
4.3%
Other values (2) 288
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
4 349
10.0%
2 337
9.6%
0 317
9.1%
1 308
8.8%
5 305
8.7%
3 293
8.4%
6 153
 
4.4%
9 150
 
4.3%
Other values (2) 288
8.2%
Distinct337
Distinct (%)67.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T01:35:24.506516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.08
Min length4

Characters and Unicode

Total characters2040
Distinct characters11
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

Unique230 ?
Unique (%)46.0%

Sample

1st row3553
2nd row4216
3rd row3723
4th row4232
5th row4019
ValueCountFrequency (%)
4094 7
 
1.4%
2710 6
 
1.2%
4055 6
 
1.2%
3432 6
 
1.2%
3535 5
 
1.0%
3745 5
 
1.0%
3740 5
 
1.0%
4144 4
 
0.8%
3723 4
 
0.8%
4043 4
 
0.8%
Other values (327) 448
89.6%
2023-12-13T01:35:25.119556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 323
15.8%
3 283
13.9%
5 250
12.3%
0 201
9.9%
6 187
9.2%
9 175
8.6%
2 164
8.0%
1 153
7.5%
7 149
7.3%
8 115
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
98.0%
Uppercase Letter 40
 
2.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 323
16.2%
3 283
14.1%
5 250
12.5%
0 201
10.1%
6 187
9.3%
9 175
8.8%
2 164
8.2%
1 153
7.6%
7 149
7.4%
8 115
 
5.8%
Uppercase Letter
ValueCountFrequency (%)
C 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
98.0%
Latin 40
 
2.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 323
16.2%
3 283
14.1%
5 250
12.5%
0 201
10.1%
6 187
9.3%
9 175
8.8%
2 164
8.2%
1 153
7.6%
7 149
7.4%
8 115
 
5.8%
Latin
ValueCountFrequency (%)
C 40
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2040
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 323
15.8%
3 283
13.9%
5 250
12.3%
0 201
9.9%
6 187
9.2%
9 175
8.6%
2 164
8.0%
1 153
7.5%
7 149
7.3%
8 115
 
5.6%
Distinct494
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T01:35:25.596926image/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

Unique489 ?
Unique (%)97.8%

Sample

1st row52:38.0
2nd row27:32.1
3rd row30:28.9
4th row02:57.2
5th row05:45.4
ValueCountFrequency (%)
53:40.6 3
 
0.6%
35:46.4 2
 
0.4%
30:29.2 2
 
0.4%
24:10.3 2
 
0.4%
07:24.1 2
 
0.4%
55:27.1 1
 
0.2%
39:05.3 1
 
0.2%
52:39.4 1
 
0.2%
42:44.3 1
 
0.2%
11:39.1 1
 
0.2%
Other values (484) 484
96.8%
2023-12-13T01:35:26.243930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
4 342
9.8%
5 331
9.5%
3 320
9.1%
1 320
9.1%
2 314
9.0%
0 308
8.8%
9 153
 
4.4%
8 143
 
4.1%
Other values (2) 269
7.7%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 342
13.7%
5 331
13.2%
3 320
12.8%
1 320
12.8%
2 314
12.6%
0 308
12.3%
9 153
6.1%
8 143
5.7%
6 141
5.6%
7 128
 
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%
4 342
9.8%
5 331
9.5%
3 320
9.1%
1 320
9.1%
2 314
9.0%
0 308
8.8%
9 153
 
4.4%
8 143
 
4.1%
Other values (2) 269
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
4 342
9.8%
5 331
9.5%
3 320
9.1%
1 320
9.1%
2 314
9.0%
0 308
8.8%
9 153
 
4.4%
8 143
 
4.1%
Other values (2) 269
7.7%
Distinct329
Distinct (%)65.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-13T01:35:26.745042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.078
Min length4

Characters and Unicode

Total characters2039
Distinct characters11
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

Unique218 ?
Unique (%)43.6%

Sample

1st row3553
2nd row4216
3rd row3723
4th row4232
5th row4019
ValueCountFrequency (%)
4094 7
 
1.4%
2710 6
 
1.2%
4055 6
 
1.2%
3745 5
 
1.0%
3740 5
 
1.0%
3750 5
 
1.0%
3535 5
 
1.0%
3746 4
 
0.8%
3723 4
 
0.8%
4144 4
 
0.8%
Other values (319) 449
89.8%
2023-12-13T01:35:27.368925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 331
16.2%
3 294
14.4%
5 255
12.5%
0 198
9.7%
9 178
8.7%
6 167
8.2%
7 155
7.6%
2 154
7.6%
1 151
7.4%
8 117
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
98.1%
Uppercase Letter 39
 
1.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 331
16.6%
3 294
14.7%
5 255
12.8%
0 198
9.9%
9 178
8.9%
6 167
8.3%
7 155
7.8%
2 154
7.7%
1 151
7.5%
8 117
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
C 39
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
98.1%
Latin 39
 
1.9%

Most frequent character per script

Common
ValueCountFrequency (%)
4 331
16.6%
3 294
14.7%
5 255
12.8%
0 198
9.9%
9 178
8.9%
6 167
8.3%
7 155
7.8%
2 154
7.7%
1 151
7.5%
8 117
 
5.9%
Latin
ValueCountFrequency (%)
C 39
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2039
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 331
16.2%
3 294
14.4%
5 255
12.5%
0 198
9.7%
9 178
8.7%
6 167
8.2%
7 155
7.6%
2 154
7.6%
1 151
7.4%
8 117
 
5.7%

Sample

상담ID업무구분코드상담일자상담일련번호상담기업개요ID상담채널구분코드일반특별구분코드직위재구분코드상담금액거래방법코드건별구분코드취급종류코드취급방법코드보증구분코드자금용도코드보증비율기업거래구분코드미확정신청기한명상담결과구분코드상담결과상세상태코드상담심의여부상담직원번호상담변경사유코드상담팀코드보증신청처리상태코드신청기한구분코드신청기한년수신청기한월수약정방법코드한도방법건별발급기한책임종료일자삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
09dnSXLsKjLG00:00.019dnSXLrSVb11195000000111111951발급후1년813553241100001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N431:01.1355352:38.03553
19dnSZ3cc0sG00:00.019dnSZ3a2IR11115000000011101101<NA>82421641000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N530:37.0421627:32.14216
29dnS0fbweIG00:00.019dnS0e9VvK11010<NA>81<NA>1000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N130:28.9372330:28.93723
39dnSUvJ26OG00:00.019dnSUvI2yT1111550000000111101<NA>112423231000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N630:25.7423202:57.24232
49dnSYCLyKUG00:00.019dnSYCKgh0111300000000111202<NA>112401921000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N730:15.7401905:45.44019
59dnS0bA8wsG00:00.019dnS0bAGOW111285000000131131952발급후1년112<NA>111010001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N329:58.3560129:31.35601
69dnSXlyKu4G00:00.019dnSXlxRzm11195000000111111951발급후1년11233684111010001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N729:49.4336846:15.23368
79dnSZScO74G00:00.019dnSZSb2Ww111200000000111101<NA>82405011000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N529:18.0405024:49.94050
89dnJBK3co8G00:00.019dnJBK11NJ111702000000111202<NA>82413431000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N527:42.7513616:34.74134
99dnlkd3D0bG00:00.019dnlkd2Ccb111200000000111101<NA>82405931000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N527:41.2405908:47.34059
상담ID업무구분코드상담일자상담일련번호상담기업개요ID상담채널구분코드일반특별구분코드직위재구분코드상담금액거래방법코드건별구분코드취급종류코드취급방법코드보증구분코드자금용도코드보증비율기업거래구분코드미확정신청기한명상담결과구분코드상담결과상세상태코드상담심의여부상담직원번호상담변경사유코드상담팀코드보증신청처리상태코드신청기한구분코드신청기한년수신청기한월수약정방법코드한도방법건별발급기한책임종료일자삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
4909dnOmQElnwG00:00.019dnOmQDKae11168000000131131852발급후1년112<NA>111010001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N500:46.09C74122:11.99C741
4919dnOmb2VzAG00:00.029dnOmb13kK111180000000111211901발급후1년1124186311100001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N600:12.9418612:16.84186
4929dnLcgILv1G00:00.019dnLcgHoOy11150000000111101<NA>112250011000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N1059:49.4343220:43.63055
4939dnOn5fh7FG00:00.019dnOn5emIj11193500000111101<NA>715398214000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N656:31.4398241:08.53982
4949dnOoHrtGuG00:00.019dnOoHqD2F111300000000111101<NA>112395011000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N556:18.3395050:32.93950
4959dnOozFESrG00:00.019dnOozEQ7G111150000000111101<NA>112365011000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N553:12.9365048:38.03650
4969dnOojLk4RG00:00.019dnOojKk3C111300000000111101<NA>112307811000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N652:08.8307844:43.03078
4979dnOoClc7qG00:00.019dnOoCkD0C111228000004311991801발급후1년112<NA>111010001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N351:08.6600849:12.56008
4989dnN8KIHyCG00:00.019dnN8KHHZN11195000000111121952발급후1년11260801111010001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N750:55.5608047:03.26080
4999dnOohBxNUG00:00.019dnOohAv0F11010<NA>81<NA>1000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000Y250:50.2420844:11.14208