Overview

Dataset statistics

Number of variables21
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory87.0 KiB
Average record size in memory178.3 B

Variable types

Categorical5
DateTime4
Numeric7
Boolean1
Text4

Dataset

Description해당 데이터는 신용보증기금 보증부문의 해지적립금 취소 정보를 확인하실 수 있는 자료이오니 참고해 주시기를 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15092628/fileData.do

Alerts

업무구분코드 has constant value ""Constant
실행해지기표일자 has constant value ""Constant
차감일련번호 has constant value ""Constant
차감일자 has constant value ""Constant
본건관련여부 has constant value ""Constant
유효개시일자 has constant value ""Constant
유효종료일자 has constant value ""Constant
이력일련번호 is highly imbalanced (86.4%)Imbalance
통화코드 is highly imbalanced (97.9%)Imbalance
최종환율 is highly imbalanced (97.9%)Imbalance
차감발생통화별금액 has 493 (98.6%) zerosZeros
차감발생환산금액 has 493 (98.6%) zerosZeros
차감통화별잔액 has 493 (98.6%) zerosZeros
차감환산잔액 has 493 (98.6%) zerosZeros
미결통화별잔액 has 493 (98.6%) zerosZeros

Reproduction

Analysis started2023-12-12 03:06:24.842183
Analysis finished2023-12-12 03:06:25.176548
Duration0.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

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

2023-12-12T12:06:25.266631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:06:25.386809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
g 500
100.0%

실행해지기표일자
Date

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2023-12-12 00:00:00
Maximum2023-12-12 00:00:00
2023-12-12T12:06:25.493790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:06:25.617962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

실행해지기표일련번호
Real number (ℝ)

Distinct6
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.39
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T12:06:25.768165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.84813836
Coefficient of variation (CV)0.61017148
Kurtosis12.690688
Mean1.39
Median Absolute Deviation (MAD)0
Skewness3.1969005
Sum695
Variance0.71933868
MonotonicityNot monotonic
2023-12-12T12:06:26.001713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 371
74.2%
2 96
 
19.2%
4 14
 
2.8%
3 13
 
2.6%
6 5
 
1.0%
7 1
 
0.2%
ValueCountFrequency (%)
1 371
74.2%
2 96
 
19.2%
3 13
 
2.6%
4 14
 
2.8%
6 5
 
1.0%
7 1
 
0.2%
ValueCountFrequency (%)
7 1
 
0.2%
6 5
 
1.0%
4 14
 
2.8%
3 13
 
2.6%
2 96
 
19.2%
1 371
74.2%

차감일련번호
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

2023-12-12T12:06:26.196955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:06:26.337070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 500
100.0%

이력일련번호
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
480 
3
 
17
5
 
2
7
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
1 480
96.0%
3 17
 
3.4%
5 2
 
0.4%
7 1
 
0.2%

Length

2023-12-12T12:06:26.494638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:06:26.656476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 480
96.0%
3 17
 
3.4%
5 2
 
0.4%
7 1
 
0.2%

통화코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
KRW
499 
USD
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
KRW 499
99.8%
USD 1
 
0.2%

Length

2023-12-12T12:06:26.816260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:06:26.975935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
krw 499
99.8%
usd 1
 
0.2%

최종환율
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1.0
499 
1247.7
 
1

Length

Max length6
Median length3
Mean length3.006
Min length3

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 499
99.8%
1247.7 1
 
0.2%

Length

2023-12-12T12:06:27.143942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:06:27.313942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 499
99.8%
1247.7 1
 
0.2%

차감발생통화별금액
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean310800
Minimum0
Maximum80000000
Zeros493
Zeros (%)98.6%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T12:06:27.441082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum80000000
Range80000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4086948.4
Coefficient of variation (CV)13.14977
Kurtosis308.57465
Mean310800
Median Absolute Deviation (MAD)0
Skewness16.88298
Sum1.554 × 108
Variance1.6703147 × 1013
MonotonicityNot monotonic
2023-12-12T12:06:27.618338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 493
98.6%
14400000 1
 
0.2%
80000000 1
 
0.2%
8550000 1
 
0.2%
40000000 1
 
0.2%
9900000 1
 
0.2%
2125000 1
 
0.2%
425000 1
 
0.2%
ValueCountFrequency (%)
0 493
98.6%
425000 1
 
0.2%
2125000 1
 
0.2%
8550000 1
 
0.2%
9900000 1
 
0.2%
14400000 1
 
0.2%
40000000 1
 
0.2%
80000000 1
 
0.2%
ValueCountFrequency (%)
80000000 1
 
0.2%
40000000 1
 
0.2%
14400000 1
 
0.2%
9900000 1
 
0.2%
8550000 1
 
0.2%
2125000 1
 
0.2%
425000 1
 
0.2%
0 493
98.6%

차감발생환산금액
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean310800
Minimum0
Maximum80000000
Zeros493
Zeros (%)98.6%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T12:06:27.785397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum80000000
Range80000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4086948.4
Coefficient of variation (CV)13.14977
Kurtosis308.57465
Mean310800
Median Absolute Deviation (MAD)0
Skewness16.88298
Sum1.554 × 108
Variance1.6703147 × 1013
MonotonicityNot monotonic
2023-12-12T12:06:27.978066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 493
98.6%
14400000 1
 
0.2%
80000000 1
 
0.2%
8550000 1
 
0.2%
40000000 1
 
0.2%
9900000 1
 
0.2%
2125000 1
 
0.2%
425000 1
 
0.2%
ValueCountFrequency (%)
0 493
98.6%
425000 1
 
0.2%
2125000 1
 
0.2%
8550000 1
 
0.2%
9900000 1
 
0.2%
14400000 1
 
0.2%
40000000 1
 
0.2%
80000000 1
 
0.2%
ValueCountFrequency (%)
80000000 1
 
0.2%
40000000 1
 
0.2%
14400000 1
 
0.2%
9900000 1
 
0.2%
8550000 1
 
0.2%
2125000 1
 
0.2%
425000 1
 
0.2%
0 493
98.6%

차감통화별잔액
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean310800
Minimum0
Maximum80000000
Zeros493
Zeros (%)98.6%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T12:06:28.189009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum80000000
Range80000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4086948.4
Coefficient of variation (CV)13.14977
Kurtosis308.57465
Mean310800
Median Absolute Deviation (MAD)0
Skewness16.88298
Sum1.554 × 108
Variance1.6703147 × 1013
MonotonicityNot monotonic
2023-12-12T12:06:28.387907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 493
98.6%
14400000 1
 
0.2%
80000000 1
 
0.2%
8550000 1
 
0.2%
40000000 1
 
0.2%
9900000 1
 
0.2%
2125000 1
 
0.2%
425000 1
 
0.2%
ValueCountFrequency (%)
0 493
98.6%
425000 1
 
0.2%
2125000 1
 
0.2%
8550000 1
 
0.2%
9900000 1
 
0.2%
14400000 1
 
0.2%
40000000 1
 
0.2%
80000000 1
 
0.2%
ValueCountFrequency (%)
80000000 1
 
0.2%
40000000 1
 
0.2%
14400000 1
 
0.2%
9900000 1
 
0.2%
8550000 1
 
0.2%
2125000 1
 
0.2%
425000 1
 
0.2%
0 493
98.6%

차감환산잔액
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean310800
Minimum0
Maximum80000000
Zeros493
Zeros (%)98.6%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T12:06:28.581710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum80000000
Range80000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4086948.4
Coefficient of variation (CV)13.14977
Kurtosis308.57465
Mean310800
Median Absolute Deviation (MAD)0
Skewness16.88298
Sum1.554 × 108
Variance1.6703147 × 1013
MonotonicityNot monotonic
2023-12-12T12:06:28.766908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 493
98.6%
14400000 1
 
0.2%
80000000 1
 
0.2%
8550000 1
 
0.2%
40000000 1
 
0.2%
9900000 1
 
0.2%
2125000 1
 
0.2%
425000 1
 
0.2%
ValueCountFrequency (%)
0 493
98.6%
425000 1
 
0.2%
2125000 1
 
0.2%
8550000 1
 
0.2%
9900000 1
 
0.2%
14400000 1
 
0.2%
40000000 1
 
0.2%
80000000 1
 
0.2%
ValueCountFrequency (%)
80000000 1
 
0.2%
40000000 1
 
0.2%
14400000 1
 
0.2%
9900000 1
 
0.2%
8550000 1
 
0.2%
2125000 1
 
0.2%
425000 1
 
0.2%
0 493
98.6%

차감일자
Date

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2023-12-12 00:00:00
Maximum2023-12-12 00:00:00
2023-12-12T12:06:28.927696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:06:29.073235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

본건관련여부
Boolean

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size632.0 B
True
500 
ValueCountFrequency (%)
True 500
100.0%
2023-12-12T12:06:29.204479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

미결통화별잔액
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean310800
Minimum0
Maximum80000000
Zeros493
Zeros (%)98.6%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T12:06:29.335447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum80000000
Range80000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4086948.4
Coefficient of variation (CV)13.14977
Kurtosis308.57465
Mean310800
Median Absolute Deviation (MAD)0
Skewness16.88298
Sum1.554 × 108
Variance1.6703147 × 1013
MonotonicityNot monotonic
2023-12-12T12:06:29.515787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 493
98.6%
14400000 1
 
0.2%
80000000 1
 
0.2%
8550000 1
 
0.2%
40000000 1
 
0.2%
9900000 1
 
0.2%
2125000 1
 
0.2%
425000 1
 
0.2%
ValueCountFrequency (%)
0 493
98.6%
425000 1
 
0.2%
2125000 1
 
0.2%
8550000 1
 
0.2%
9900000 1
 
0.2%
14400000 1
 
0.2%
40000000 1
 
0.2%
80000000 1
 
0.2%
ValueCountFrequency (%)
80000000 1
 
0.2%
40000000 1
 
0.2%
14400000 1
 
0.2%
9900000 1
 
0.2%
8550000 1
 
0.2%
2125000 1
 
0.2%
425000 1
 
0.2%
0 493
98.6%

유효개시일자
Date

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2023-12-12 00:00:00
Maximum2023-12-12 00:00:00
2023-12-12T12:06:29.659289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:06:29.794619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

유효종료일자
Date

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2023-12-12 00:00:00
Maximum2023-12-12 00:00:00
2023-12-12T12:06:29.926275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:06:30.072529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

최종수정수
Real number (ℝ)

Distinct7
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.144
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T12:06:30.240272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum7
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5761318
Coefficient of variation (CV)0.50361172
Kurtosis38.874733
Mean1.144
Median Absolute Deviation (MAD)0
Skewness5.5443068
Sum572
Variance0.33192786
MonotonicityNot monotonic
2023-12-12T12:06:30.421793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 459
91.8%
2 21
 
4.2%
3 15
 
3.0%
4 2
 
0.4%
5 1
 
0.2%
7 1
 
0.2%
6 1
 
0.2%
ValueCountFrequency (%)
1 459
91.8%
2 21
 
4.2%
3 15
 
3.0%
4 2
 
0.4%
5 1
 
0.2%
6 1
 
0.2%
7 1
 
0.2%
ValueCountFrequency (%)
7 1
 
0.2%
6 1
 
0.2%
5 1
 
0.2%
4 2
 
0.4%
3 15
 
3.0%
2 21
 
4.2%
1 459
91.8%

처리시각
Text

Distinct497
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T12:06:30.915487image/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 row30:20.7
2nd row30:01.2
3rd row46:27.3
4th row57:45.6
5th row01:37.8
ValueCountFrequency (%)
21:14.9 2
 
0.4%
26:11.4 2
 
0.4%
56:46.0 2
 
0.4%
56:28.6 1
 
0.2%
18:39.4 1
 
0.2%
47:31.1 1
 
0.2%
55:28.6 1
 
0.2%
35:08.5 1
 
0.2%
11:30.0 1
 
0.2%
11:54.8 1
 
0.2%
Other values (487) 487
97.4%
2023-12-12T12:06:31.608670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
4 332
9.5%
3 329
9.4%
5 323
9.2%
0 309
8.8%
2 300
8.6%
1 287
8.2%
9 166
 
4.7%
6 159
 
4.5%
Other values (2) 295
8.4%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 332
13.3%
3 329
13.2%
5 323
12.9%
0 309
12.4%
2 300
12.0%
1 287
11.5%
9 166
6.6%
6 159
6.4%
8 148
5.9%
7 147
5.9%
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%
4 332
9.5%
3 329
9.4%
5 323
9.2%
0 309
8.8%
2 300
8.6%
1 287
8.2%
9 166
 
4.7%
6 159
 
4.5%
Other values (2) 295
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
4 332
9.5%
3 329
9.4%
5 323
9.2%
0 309
8.8%
2 300
8.6%
1 287
8.2%
9 166
 
4.7%
6 159
 
4.5%
Other values (2) 295
8.4%

처리직원번호
Text

Distinct332
Distinct (%)66.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T12:06:32.140015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.036
Min length4

Characters and Unicode

Total characters2018
Distinct characters12
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

Unique228 ?
Unique (%)45.6%

Sample

1st row6124
2nd row6124
3rd row6008
4th row9C732
5th row6184
ValueCountFrequency (%)
5438 7
 
1.4%
5895 6
 
1.2%
5827 6
 
1.2%
5932 5
 
1.0%
5987 5
 
1.0%
4830 5
 
1.0%
5701 5
 
1.0%
4843 4
 
0.8%
5587 4
 
0.8%
5901 4
 
0.8%
Other values (322) 449
89.8%
2023-12-12T12:06:32.868554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 388
19.2%
4 261
12.9%
6 197
9.8%
8 194
9.6%
0 193
9.6%
3 188
9.3%
9 161
8.0%
1 148
 
7.3%
2 137
 
6.8%
7 133
 
6.6%
Other values (2) 18
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
99.1%
Uppercase Letter 18
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 388
19.4%
4 261
13.1%
6 197
9.8%
8 194
9.7%
0 193
9.7%
3 188
9.4%
9 161
8.1%
1 148
 
7.4%
2 137
 
6.9%
7 133
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
C 17
94.4%
B 1
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
99.1%
Latin 18
 
0.9%

Most frequent character per script

Common
ValueCountFrequency (%)
5 388
19.4%
4 261
13.1%
6 197
9.8%
8 194
9.7%
0 193
9.7%
3 188
9.4%
9 161
8.1%
1 148
 
7.4%
2 137
 
6.9%
7 133
 
6.7%
Latin
ValueCountFrequency (%)
C 17
94.4%
B 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2018
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 388
19.2%
4 261
12.9%
6 197
9.8%
8 194
9.6%
0 193
9.6%
3 188
9.3%
9 161
8.0%
1 148
 
7.3%
2 137
 
6.8%
7 133
 
6.6%
Other values (2) 18
 
0.9%

최초처리시각
Text

Distinct477
Distinct (%)95.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T12:06:33.409424image/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

Unique457 ?
Unique (%)91.4%

Sample

1st row30:20.7
2nd row30:01.2
3rd row46:27.3
4th row57:45.6
5th row01:37.8
ValueCountFrequency (%)
15:26.9 4
 
0.8%
03:45.3 3
 
0.6%
39:47.5 2
 
0.4%
33:09.0 2
 
0.4%
35:32.3 2
 
0.4%
27:04.1 2
 
0.4%
39:36.8 2
 
0.4%
33:53.9 2
 
0.4%
33:48.7 2
 
0.4%
43:01.6 2
 
0.4%
Other values (467) 477
95.4%
2023-12-12T12:06:34.116073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
3 350
10.0%
4 327
9.3%
5 314
9.0%
0 306
8.7%
2 295
8.4%
1 284
8.1%
9 173
 
4.9%
6 159
 
4.5%
Other values (2) 292
8.3%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 350
14.0%
4 327
13.1%
5 314
12.6%
0 306
12.2%
2 295
11.8%
1 284
11.4%
9 173
6.9%
6 159
6.4%
8 150
6.0%
7 142
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%
3 350
10.0%
4 327
9.3%
5 314
9.0%
0 306
8.7%
2 295
8.4%
1 284
8.1%
9 173
 
4.9%
6 159
 
4.5%
Other values (2) 292
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
3 350
10.0%
4 327
9.3%
5 314
9.0%
0 306
8.7%
2 295
8.4%
1 284
8.1%
9 173
 
4.9%
6 159
 
4.5%
Other values (2) 292
8.3%

최초처리직원번호
Text

Distinct331
Distinct (%)66.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T12:06:34.692409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.036
Min length4

Characters and Unicode

Total characters2018
Distinct characters12
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

Unique226 ?
Unique (%)45.2%

Sample

1st row6124
2nd row6124
3rd row6008
4th row9C732
5th row6184
ValueCountFrequency (%)
5438 7
 
1.4%
5895 6
 
1.2%
5827 6
 
1.2%
5932 5
 
1.0%
5701 5
 
1.0%
4830 5
 
1.0%
5987 5
 
1.0%
4843 4
 
0.8%
5152 4
 
0.8%
4376 4
 
0.8%
Other values (321) 449
89.8%
2023-12-12T12:06:35.394644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 388
19.2%
4 261
12.9%
6 198
9.8%
0 195
9.7%
8 193
9.6%
3 185
9.2%
9 162
8.0%
1 148
 
7.3%
2 138
 
6.8%
7 132
 
6.5%
Other values (2) 18
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2000
99.1%
Uppercase Letter 18
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 388
19.4%
4 261
13.1%
6 198
9.9%
0 195
9.8%
8 193
9.7%
3 185
9.2%
9 162
8.1%
1 148
 
7.4%
2 138
 
6.9%
7 132
 
6.6%
Uppercase Letter
ValueCountFrequency (%)
C 17
94.4%
B 1
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2000
99.1%
Latin 18
 
0.9%

Most frequent character per script

Common
ValueCountFrequency (%)
5 388
19.4%
4 261
13.1%
6 198
9.9%
0 195
9.8%
8 193
9.7%
3 185
9.2%
9 162
8.1%
1 148
 
7.4%
2 138
 
6.9%
7 132
 
6.6%
Latin
ValueCountFrequency (%)
C 17
94.4%
B 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2018
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 388
19.2%
4 261
12.9%
6 198
9.8%
0 195
9.7%
8 193
9.6%
3 185
9.2%
9 162
8.0%
1 148
 
7.3%
2 138
 
6.8%
7 132
 
6.5%
Other values (2) 18
 
0.9%

Sample

업무구분코드실행해지기표일자실행해지기표일련번호차감일련번호이력일련번호통화코드최종환율차감발생통화별금액차감발생환산금액차감통화별잔액차감환산잔액차감일자본건관련여부미결통화별잔액유효개시일자유효종료일자최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
0G00:00.0111KRW1.0000000:00.0Y000:00.000:00.0130:20.7612430:20.76124
1G00:00.0111KRW1.0000000:00.0Y000:00.000:00.0130:01.2612430:01.26124
2G00:00.0111KRW1.0000000:00.0Y000:00.000:00.0146:27.3600846:27.36008
3G00:00.0111KRW1.0000000:00.0Y000:00.000:00.0157:45.69C73257:45.69C732
4G00:00.0111KRW1.0000000:00.0Y000:00.000:00.0101:37.8618401:37.86184
5G00:00.0111KRW1.0000000:00.0Y000:00.000:00.0152:39.6601252:39.66012
6G00:00.0211KRW1.0000000:00.0Y000:00.000:00.0143:07.1480643:07.14806
7G00:00.0211KRW1.0000000:00.0Y000:00.000:00.0148:54.8473648:54.84736
8G00:00.0111KRW1.0000000:00.0Y000:00.000:00.0326:32.6533859:43.66094
9G00:00.0113KRW1.0000000:00.0Y000:00.000:00.0224:47.6609459:43.66094
업무구분코드실행해지기표일자실행해지기표일련번호차감일련번호이력일련번호통화코드최종환율차감발생통화별금액차감발생환산금액차감통화별잔액차감환산잔액차감일자본건관련여부미결통화별잔액유효개시일자유효종료일자최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
490G00:00.0111KRW1.0000000:00.0Y000:00.000:00.0105:20.3591605:20.35916
491G00:00.0113KRW1.0000000:00.0Y000:00.000:00.0222:20.39C06848:22.49C068
492G00:00.0111KRW1.0000000:00.0Y000:00.000:00.0135:02.9587635:02.95876
493G00:00.0111KRW1.0000000:00.0Y000:00.000:00.0133:42.5578033:42.55780
494G00:00.0111KRW1.0000000:00.0Y000:00.000:00.0124:13.4566824:13.45668
495G00:00.0111KRW1.0000000:00.0Y000:00.000:00.0123:30.3566823:30.35668
496G00:00.0111KRW1.0000000:00.0Y000:00.000:00.0119:38.7566819:38.75668
497G00:00.0111KRW1.0000000:00.0Y000:00.000:00.0115:40.1509915:40.15099
498G00:00.0111KRW1.0000000:00.0Y000:00.000:00.0118:26.8570118:26.85701
499G00:00.0111KRW1.0000000:00.0Y000:00.000:00.0125:50.8547125:50.85471