Overview

Dataset statistics

Number of variables20
Number of observations500
Missing cells58
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory81.7 KiB
Average record size in memory167.3 B

Variable types

Categorical10
Text2
Numeric7
Boolean1

Dataset

Description해당 파일 데이터는 신용보증기금의 보증고객창업기업정보에 대해 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15092890/fileData.do

Alerts

업무구분코드 has constant value ""Constant
성과공유금액유예기한 has constant value ""Constant
삭제여부 has constant value ""Constant
성과공유금액최초납부기한 is highly overall correlated with 한도설정금액 and 5 other fieldsHigh correlation
최초처리시각 is highly overall correlated with 최초처리직원번호High correlation
성과공유금액분납신청여부 is highly overall correlated with 한도설정금액 and 5 other fieldsHigh correlation
최초처리직원번호 is highly overall correlated with 최초처리시각High correlation
4기평가년도 is highly overall correlated with 5기평가년도High correlation
5기평가년도 is highly overall correlated with 4기평가년도High correlation
한도설정금액 is highly overall correlated with 건별발급금액 and 4 other fieldsHigh correlation
건별발급금액 is highly overall correlated with 한도설정금액 and 2 other fieldsHigh correlation
성과공유금액납부예정금액 is highly overall correlated with 한도설정금액 and 4 other fieldsHigh correlation
성과공유금액약정체결여부 is highly overall correlated with 중단일자 and 2 other fieldsHigh correlation
성과공유금액약정체결사유코드 is highly overall correlated with 중단일자High correlation
중단일자 is highly overall correlated with 한도설정금액 and 4 other fieldsHigh correlation
성과공유금액분납예정일자 is highly overall correlated with 성과공유금액납부예정금액 and 1 other fieldsHigh correlation
성과공유금액약정체결여부 is highly imbalanced (51.4%)Imbalance
성과공유금액약정체결사유코드 is highly imbalanced (54.6%)Imbalance
성과공유금액분납예정일자 is highly imbalanced (68.9%)Imbalance
최초처리시각 is highly imbalanced (84.0%)Imbalance
최초처리직원번호 is highly imbalanced (83.8%)Imbalance
4기평가년도 has 29 (5.8%) missing valuesMissing
5기평가년도 has 29 (5.8%) missing valuesMissing
원장번호 has unique valuesUnique
한도설정금액 has 159 (31.8%) zerosZeros
건별발급금액 has 159 (31.8%) zerosZeros
성과공유금액납부예정금액 has 159 (31.8%) zerosZeros

Reproduction

Analysis started2023-12-12 00:29:28.054165
Analysis finished2023-12-12 00:29:34.440876
Duration6.39 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

2023-12-12T09:29:34.501584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:29:34.592957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
g 500
100.0%

원장번호
Text

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T09:29:34.849218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters6000
Distinct characters35
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

Unique500 ?
Unique (%)100.0%

Sample

1st rowTIF200700832
2nd rowIIA200801114
3rd rowTHZ200801078
4th rowTIE200800236
5th rowTAQ201200149
ValueCountFrequency (%)
tif200700832 1
 
0.2%
tat200701415 1
 
0.2%
tbk201000252 1
 
0.2%
tqa201100021 1
 
0.2%
thi200701012 1
 
0.2%
thu200800654 1
 
0.2%
tal201001990 1
 
0.2%
thn201100351 1
 
0.2%
ipa200800210 1
 
0.2%
iaa200800315 1
 
0.2%
Other values (490) 490
98.0%
2023-12-12T09:29:35.306917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2084
34.7%
2 668
 
11.1%
8 399
 
6.7%
T 324
 
5.4%
A 295
 
4.9%
7 292
 
4.9%
1 288
 
4.8%
I 269
 
4.5%
9 161
 
2.7%
4 159
 
2.6%
Other values (25) 1061
17.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4500
75.0%
Uppercase Letter 1500
 
25.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 324
21.6%
A 295
19.7%
I 269
17.9%
H 115
 
7.7%
P 69
 
4.6%
B 63
 
4.2%
C 50
 
3.3%
Q 42
 
2.8%
D 33
 
2.2%
N 32
 
2.1%
Other values (15) 208
13.9%
Decimal Number
ValueCountFrequency (%)
0 2084
46.3%
2 668
 
14.8%
8 399
 
8.9%
7 292
 
6.5%
1 288
 
6.4%
9 161
 
3.6%
4 159
 
3.5%
3 157
 
3.5%
6 147
 
3.3%
5 145
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Common 4500
75.0%
Latin 1500
 
25.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 324
21.6%
A 295
19.7%
I 269
17.9%
H 115
 
7.7%
P 69
 
4.6%
B 63
 
4.2%
C 50
 
3.3%
Q 42
 
2.8%
D 33
 
2.2%
N 32
 
2.1%
Other values (15) 208
13.9%
Common
ValueCountFrequency (%)
0 2084
46.3%
2 668
 
14.8%
8 399
 
8.9%
7 292
 
6.5%
1 288
 
6.4%
9 161
 
3.6%
4 159
 
3.5%
3 157
 
3.5%
6 147
 
3.3%
5 145
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2084
34.7%
2 668
 
11.1%
8 399
 
6.7%
T 324
 
5.4%
A 295
 
4.9%
7 292
 
4.9%
1 288
 
4.8%
I 269
 
4.5%
9 161
 
2.7%
4 159
 
2.6%
Other values (25) 1061
17.7%

성과공유금액약정체결여부
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Y
402 
N
93 
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
Y 402
80.4%
N 93
 
18.6%
5
 
1.0%

Length

2023-12-12T09:29:35.494632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:29:35.668388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
y 402
81.2%
n 93
 
18.8%

성과공유금액약정체결사유코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
418 
74 
2
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 418
83.6%
74
 
14.8%
2 8
 
1.6%

Length

2023-12-12T09:29:35.782180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:29:35.889375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 418
98.1%
2 8
 
1.9%

4기평가년도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)1.7%
Missing29
Missing (%)5.8%
Infinite0
Infinite (%)0.0%
Mean2010.845
Minimum2009
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T09:29:36.011611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2009
5-th percentile2010
Q12010
median2011
Q32011
95-th percentile2012
Maximum2016
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.86256147
Coefficient of variation (CV)0.00042895473
Kurtosis8.8657235
Mean2010.845
Median Absolute Deviation (MAD)0
Skewness2.1620037
Sum947108
Variance0.74401229
MonotonicityNot monotonic
2023-12-12T09:29:36.175398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2011 283
56.6%
2010 143
28.6%
2012 21
 
4.2%
2013 9
 
1.8%
2014 7
 
1.4%
2009 4
 
0.8%
2015 2
 
0.4%
2016 2
 
0.4%
(Missing) 29
 
5.8%
ValueCountFrequency (%)
2009 4
 
0.8%
2010 143
28.6%
2011 283
56.6%
2012 21
 
4.2%
2013 9
 
1.8%
2014 7
 
1.4%
2015 2
 
0.4%
2016 2
 
0.4%
ValueCountFrequency (%)
2016 2
 
0.4%
2015 2
 
0.4%
2014 7
 
1.4%
2013 9
 
1.8%
2012 21
 
4.2%
2011 283
56.6%
2010 143
28.6%
2009 4
 
0.8%

5기평가년도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)1.7%
Missing29
Missing (%)5.8%
Infinite0
Infinite (%)0.0%
Mean2011.845
Minimum2010
Maximum2017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T09:29:36.324708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2011
Q12011
median2012
Q32012
95-th percentile2013
Maximum2017
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.86256147
Coefficient of variation (CV)0.00042874151
Kurtosis8.8657235
Mean2011.845
Median Absolute Deviation (MAD)0
Skewness2.1620037
Sum947579
Variance0.74401229
MonotonicityNot monotonic
2023-12-12T09:29:36.461483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2012 283
56.6%
2011 143
28.6%
2013 21
 
4.2%
2014 9
 
1.8%
2015 7
 
1.4%
2010 4
 
0.8%
2016 2
 
0.4%
2017 2
 
0.4%
(Missing) 29
 
5.8%
ValueCountFrequency (%)
2010 4
 
0.8%
2011 143
28.6%
2012 283
56.6%
2013 21
 
4.2%
2014 9
 
1.8%
2015 7
 
1.4%
2016 2
 
0.4%
2017 2
 
0.4%
ValueCountFrequency (%)
2017 2
 
0.4%
2016 2
 
0.4%
2015 7
 
1.4%
2014 9
 
1.8%
2013 21
 
4.2%
2012 283
56.6%
2011 143
28.6%
2010 4
 
0.8%

중단일자
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0001-01-01 00:00:00.000000
437 
00:00.0
63 

Length

Max length26
Median length26
Mean length23.606
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 row00:00.0
5th row0001-01-01 00:00:00.000000

Common Values

ValueCountFrequency (%)
0001-01-01 00:00:00.000000 437
87.4%
00:00.0 63
 
12.6%

Length

2023-12-12T09:29:36.615880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:29:36.741752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0001-01-01 437
46.6%
00:00:00.000000 437
46.6%
00:00.0 63
 
6.7%

한도설정금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.06588 × 108
Minimum0
Maximum5 × 108
Zeros159
Zeros (%)31.8%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T09:29:36.883668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.07 × 108
Q33 × 108
95-th percentile5 × 108
Maximum5 × 108
Range5 × 108
Interquartile range (IQR)3 × 108

Descriptive statistics

Standard deviation1.7311214 × 108
Coefficient of variation (CV)0.83795836
Kurtosis-1.1219059
Mean2.06588 × 108
Median Absolute Deviation (MAD)1.07 × 108
Skewness0.2036442
Sum1.03294 × 1011
Variance2.9967814 × 1016
MonotonicityNot monotonic
2023-12-12T09:29:37.087871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 159
31.8%
300000000 153
30.6%
500000000 68
13.6%
200000000 39
 
7.8%
150000000 25
 
5.0%
100000000 17
 
3.4%
250000000 9
 
1.8%
450000000 4
 
0.8%
400000000 3
 
0.6%
60000000 2
 
0.4%
Other values (17) 21
 
4.2%
ValueCountFrequency (%)
0 159
31.8%
50000000 2
 
0.4%
60000000 2
 
0.4%
80000000 1
 
0.2%
100000000 17
 
3.4%
130000000 2
 
0.4%
135000000 1
 
0.2%
150000000 25
 
5.0%
170000000 1
 
0.2%
175000000 1
 
0.2%
ValueCountFrequency (%)
500000000 68
13.6%
470000000 1
 
0.2%
461000000 1
 
0.2%
450000000 4
 
0.8%
405000000 1
 
0.2%
400000000 3
 
0.6%
360000000 1
 
0.2%
300000000 153
30.6%
282000000 1
 
0.2%
257000000 1
 
0.2%

건별발급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct178
Distinct (%)35.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3491965 × 108
Minimum0
Maximum1.375825 × 109
Zeros159
Zeros (%)31.8%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T09:29:37.236144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median90000000
Q32 × 108
95-th percentile4.808625 × 108
Maximum1.375825 × 109
Range1.375825 × 109
Interquartile range (IQR)2 × 108

Descriptive statistics

Standard deviation1.6295354 × 108
Coefficient of variation (CV)1.2077821
Kurtosis8.3195017
Mean1.3491965 × 108
Median Absolute Deviation (MAD)90000000
Skewness2.1401311
Sum6.7459825 × 1010
Variance2.6553855 × 1016
MonotonicityNot monotonic
2023-12-12T09:29:37.414292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 159
31.8%
100000000 23
 
4.6%
300000000 21
 
4.2%
90000000 17
 
3.4%
50000000 15
 
3.0%
45000000 10
 
2.0%
200000000 10
 
2.0%
180000000 7
 
1.4%
190000000 7
 
1.4%
150000000 6
 
1.2%
Other values (168) 225
45.0%
ValueCountFrequency (%)
0 159
31.8%
9000000 1
 
0.2%
13500000 1
 
0.2%
17000000 1
 
0.2%
18000000 1
 
0.2%
19550000 1
 
0.2%
23000000 1
 
0.2%
27000000 3
 
0.6%
30000000 4
 
0.8%
31500000 1
 
0.2%
ValueCountFrequency (%)
1375825000 1
0.2%
900000000 1
0.2%
864000000 1
0.2%
732500000 1
0.2%
699500000 1
0.2%
600000000 2
0.4%
597500000 1
0.2%
578000000 1
0.2%
558000000 1
0.2%
540500000 1
0.2%

성과공유금액납부예정금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct147
Distinct (%)29.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2508654
Minimum0
Maximum10000000
Zeros159
Zeros (%)31.8%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T09:29:37.567828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1800000
Q34000000
95-th percentile8293750
Maximum10000000
Range10000000
Interquartile range (IQR)4000000

Descriptive statistics

Standard deviation2717275
Coefficient of variation (CV)1.0831605
Kurtosis0.46711973
Mean2508654
Median Absolute Deviation (MAD)1800000
Skewness1.0954535
Sum1.254327 × 109
Variance7.3835835 × 1012
MonotonicityNot monotonic
2023-12-12T09:29:37.721820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 159
31.8%
6000000 36
 
7.2%
2000000 24
 
4.8%
1800000 17
 
3.4%
1000000 16
 
3.2%
4000000 13
 
2.6%
10000000 12
 
2.4%
900000 10
 
2.0%
3000000 9
 
1.8%
3600000 7
 
1.4%
Other values (137) 197
39.4%
ValueCountFrequency (%)
0 159
31.8%
180000 1
 
0.2%
270000 1
 
0.2%
340000 1
 
0.2%
360000 1
 
0.2%
391000 1
 
0.2%
460000 1
 
0.2%
540000 3
 
0.6%
600000 4
 
0.8%
630000 1
 
0.2%
ValueCountFrequency (%)
10000000 12
2.4%
9960000 1
 
0.2%
9940000 2
 
0.4%
9910000 1
 
0.2%
9900000 1
 
0.2%
9880000 1
 
0.2%
9850000 1
 
0.2%
9605000 1
 
0.2%
9560000 1
 
0.2%
9300000 1
 
0.2%

성과공유금액최초납부기한
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
00:00.0
341 
0001-01-01 00:00:00.000000
159 

Length

Max length26
Median length7
Mean length13.042
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row00:00.0
2nd row00:00.0
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 (%)
00:00.0 341
68.2%
0001-01-01 00:00:00.000000 159
31.8%

Length

2023-12-12T09:29:37.897418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:29:38.028526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00.0 341
51.7%
0001-01-01 159
24.1%
00:00:00.000000 159
24.1%

성과공유금액분납신청여부
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
N
304 
159 
Y
37 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
N 304
60.8%
159
31.8%
Y 37
 
7.4%

Length

2023-12-12T09:29:38.145055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:29:38.261508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n 304
89.1%
y 37
 
10.9%

성과공유금액분납예정일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0001-01-01 00:00:00.000000
472 
00:00.0
 
28

Length

Max length26
Median length26
Mean length24.936
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 472
94.4%
00:00.0 28
 
5.6%

Length

2023-12-12T09:29:38.398948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:29:38.520216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0001-01-01 472
48.6%
00:00:00.000000 472
48.6%
00:00.0 28
 
2.9%

성과공유금액유예기한
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

2023-12-12T09:29:38.656523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:29:38.764577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0001-01-01 500
50.0%
00:00:00.000000 500
50.0%

삭제여부
Boolean

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
500 
ValueCountFrequency (%)
False 500
100.0%
2023-12-12T09:29:38.858835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

최종수정수
Real number (ℝ)

Distinct20
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.002
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T09:29:38.966984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile11
Maximum31
Range30
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.4072297
Coefficient of variation (CV)0.85138174
Kurtosis12.271193
Mean4.002
Median Absolute Deviation (MAD)1
Skewness2.843069
Sum2001
Variance11.609214
MonotonicityNot monotonic
2023-12-12T09:29:39.090694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2 179
35.8%
3 89
17.8%
4 51
 
10.2%
1 42
 
8.4%
5 39
 
7.8%
6 27
 
5.4%
7 20
 
4.0%
9 13
 
2.6%
11 7
 
1.4%
10 6
 
1.2%
Other values (10) 27
 
5.4%
ValueCountFrequency (%)
1 42
 
8.4%
2 179
35.8%
3 89
17.8%
4 51
 
10.2%
5 39
 
7.8%
6 27
 
5.4%
7 20
 
4.0%
8 6
 
1.2%
9 13
 
2.6%
10 6
 
1.2%
ValueCountFrequency (%)
31 1
 
0.2%
24 1
 
0.2%
18 2
 
0.4%
17 2
 
0.4%
16 2
 
0.4%
15 2
 
0.4%
14 3
0.6%
13 6
1.2%
12 2
 
0.4%
11 7
1.4%
Distinct498
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T09:29:39.484937image/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

Unique496 ?
Unique (%)99.2%

Sample

1st row05:08.4
2nd row05:37.4
3rd row08:47.6
4th row40:54.7
5th row36:10.7
ValueCountFrequency (%)
47:13.8 2
 
0.4%
22:14.3 2
 
0.4%
58:27.1 1
 
0.2%
06:53.5 1
 
0.2%
53:45.8 1
 
0.2%
12:54.2 1
 
0.2%
29:03.2 1
 
0.2%
50:56.1 1
 
0.2%
37:40.1 1
 
0.2%
46:39.1 1
 
0.2%
Other values (488) 488
97.6%
2023-12-12T09:29:39.970217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
1 333
9.5%
2 332
9.5%
5 322
9.2%
0 319
9.1%
4 304
8.7%
3 294
8.4%
7 171
 
4.9%
6 152
 
4.3%
Other values (2) 273
7.8%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 333
13.3%
2 332
13.3%
5 322
12.9%
0 319
12.8%
4 304
12.2%
3 294
11.8%
7 171
6.8%
6 152
6.1%
8 137
5.5%
9 136
5.4%
Other Punctuation
ValueCountFrequency (%)
: 500
50.0%
. 500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
1 333
9.5%
2 332
9.5%
5 322
9.2%
0 319
9.1%
4 304
8.7%
3 294
8.4%
7 171
 
4.9%
6 152
 
4.3%
Other values (2) 273
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
1 333
9.5%
2 332
9.5%
5 322
9.2%
0 319
9.1%
4 304
8.7%
3 294
8.4%
7 171
 
4.9%
6 152
 
4.3%
Other values (2) 273
7.8%

처리직원번호
Real number (ℝ)

Distinct297
Distinct (%)59.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4068.116
Minimum1019
Maximum5218
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T09:29:40.130929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1019
5-th percentile2958.65
Q13520
median4141
Q34640.25
95-th percentile5047.15
Maximum5218
Range4199
Interquartile range (IQR)1120.25

Descriptive statistics

Standard deviation720.40015
Coefficient of variation (CV)0.17708447
Kurtosis1.15649
Mean4068.116
Median Absolute Deviation (MAD)538
Skewness-0.79730112
Sum2034058
Variance518976.38
MonotonicityNot monotonic
2023-12-12T09:29:40.271973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3965 6
 
1.2%
4505 6
 
1.2%
4636 6
 
1.2%
3290 5
 
1.0%
4679 5
 
1.0%
3200 5
 
1.0%
4256 5
 
1.0%
3687 5
 
1.0%
3082 5
 
1.0%
3415 4
 
0.8%
Other values (287) 448
89.6%
ValueCountFrequency (%)
1019 3
0.6%
1886 2
0.4%
1928 1
 
0.2%
1940 1
 
0.2%
2394 2
0.4%
2398 2
0.4%
2443 1
 
0.2%
2451 1
 
0.2%
2458 1
 
0.2%
2564 4
0.8%
ValueCountFrequency (%)
5218 1
 
0.2%
5124 1
 
0.2%
5118 2
0.4%
5112 1
 
0.2%
5109 1
 
0.2%
5106 3
0.6%
5103 1
 
0.2%
5098 1
 
0.2%
5097 1
 
0.2%
5094 1
 
0.2%

최초처리시각
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct43
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
0001-01-01 00:00:00.000000
458 
47:28.9
 
1
22:30.0
 
1
04:06.7
 
1
48:27.4
 
1
Other values (38)
 
38

Length

Max length26
Median length26
Mean length24.404
Min length7

Unique

Unique42 ?
Unique (%)8.4%

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 458
91.6%
47:28.9 1
 
0.2%
22:30.0 1
 
0.2%
04:06.7 1
 
0.2%
48:27.4 1
 
0.2%
51:23.0 1
 
0.2%
42:04.7 1
 
0.2%
49:54.2 1
 
0.2%
28:00.9 1
 
0.2%
23:28.4 1
 
0.2%
Other values (33) 33
 
6.6%

Length

2023-12-12T09:29:40.399930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0001-01-01 458
47.8%
00:00:00.000000 458
47.8%
44:02.7 1
 
0.1%
55:14.6 1
 
0.1%
56:00.1 1
 
0.1%
35:11.5 1
 
0.1%
03:28.6 1
 
0.1%
54:47.1 1
 
0.1%
51:41.3 1
 
0.1%
12:54.2 1
 
0.1%
Other values (34) 34
 
3.5%

최초처리직원번호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct35
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
BATCH
458 
4191
 
4
4256
 
4
3687
 
2
3415
 
2
Other values (30)
 
30

Length

Max length5
Median length5
Mean length4.916
Min length4

Unique

Unique30 ?
Unique (%)6.0%

Sample

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

Common Values

ValueCountFrequency (%)
BATCH 458
91.6%
4191 4
 
0.8%
4256 4
 
0.8%
3687 2
 
0.4%
3415 2
 
0.4%
3670 1
 
0.2%
4876 1
 
0.2%
3599 1
 
0.2%
5046 1
 
0.2%
5076 1
 
0.2%
Other values (25) 25
 
5.0%

Length

2023-12-12T09:29:40.523158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
batch 458
91.6%
4256 4
 
0.8%
4191 4
 
0.8%
3687 2
 
0.4%
3415 2
 
0.4%
4188 1
 
0.2%
4013 1
 
0.2%
4984 1
 
0.2%
4589 1
 
0.2%
4024 1
 
0.2%
Other values (25) 25
 
5.0%

Interactions

2023-12-12T09:29:32.769573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:29.118360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:29.681970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:30.238449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:30.732029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:31.306988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:32.009881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:32.886847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:29.209032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:29.777212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:30.311403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:30.809545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:31.398274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:32.106980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:32.996439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:29.296489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:29.871321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:30.386122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:30.889698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:31.487421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:32.254642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:33.115175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:29.375358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:29.939106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:30.452518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:30.962910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:31.569682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:32.346366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:33.226492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:29.455563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:30.015015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:30.527502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:31.049467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:31.667438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:32.452245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:33.351806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:29.533786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:30.095533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:30.596463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:31.133068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:31.750660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:32.568331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:33.464718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:29.608013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:30.168889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:30.666826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:31.218204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:31.865822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:29:32.671889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:29:40.609655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
성과공유금액약정체결여부성과공유금액약정체결사유코드4기평가년도5기평가년도중단일자한도설정금액건별발급금액성과공유금액납부예정금액성과공유금액최초납부기한성과공유금액분납신청여부성과공유금액분납예정일자최종수정수처리직원번호최초처리시각최초처리직원번호
성과공유금액약정체결여부1.0000.7880.3890.3890.4940.7890.3630.5460.4690.8330.0620.0650.0860.7120.709
성과공유금액약정체결사유코드0.7881.0000.5980.5980.3230.5960.2710.4720.2970.6810.0000.3680.0000.7450.693
4기평가년도0.3890.5981.0001.0000.4240.2820.0000.1130.3850.3870.0000.0000.0000.8070.845
5기평가년도0.3890.5981.0001.0000.4240.2820.0000.1130.3850.3870.0000.0000.0000.8070.845
중단일자0.4940.3230.4240.4241.0000.5380.3660.5540.7590.3460.1030.0000.0990.4080.429
한도설정금액0.7890.5960.2820.2820.5381.0000.5270.7210.9560.9480.3150.1580.1890.0000.000
건별발급금액0.3630.2710.0000.0000.3660.5271.0000.8240.6890.6240.6240.2620.3010.0000.000
성과공유금액납부예정금액0.5460.4720.1130.1130.5540.7210.8241.0000.9410.7900.7060.5410.2220.0000.000
성과공유금액최초납부기한0.4690.2970.3850.3850.7590.9560.6890.9411.0001.0000.2340.3260.1530.4190.439
성과공유금액분납신청여부0.8330.6810.3870.3870.3460.9480.6240.7901.0001.0000.5590.3730.1710.2480.340
성과공유금액분납예정일자0.0620.0000.0000.0000.1030.3150.6240.7060.2340.5591.0000.4560.1660.0000.000
최종수정수0.0650.3680.0000.0000.0000.1580.2620.5410.3260.3730.4561.0000.3200.0000.000
처리직원번호0.0860.0000.0000.0000.0990.1890.3010.2220.1530.1710.1660.3201.0000.0000.000
최초처리시각0.7120.7450.8070.8070.4080.0000.0000.0000.4190.2480.0000.0000.0001.0001.000
최초처리직원번호0.7090.6930.8450.8450.4290.0000.0000.0000.4390.3400.0000.0000.0001.0001.000
2023-12-12T09:29:40.747882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
성과공유금액최초납부기한중단일자최초처리시각성과공유금액약정체결여부성과공유금액분납신청여부성과공유금액분납예정일자최초처리직원번호성과공유금액약정체결사유코드
성과공유금액최초납부기한1.0000.5480.3360.7210.9990.1510.3590.480
중단일자0.5481.0000.3270.7520.5530.0660.3510.518
최초처리시각0.3360.3271.0000.4580.1190.0000.9910.493
성과공유금액약정체결여부0.7210.7520.4581.0000.5080.1020.4650.450
성과공유금액분납신청여부0.9990.5530.1190.5081.0000.8270.1740.339
성과공유금액분납예정일자0.1510.0660.0000.1020.8271.0000.0000.000
최초처리직원번호0.3590.3510.9910.4650.1740.0001.0000.448
성과공유금액약정체결사유코드0.4800.5180.4930.4500.3390.0000.4481.000
2023-12-12T09:29:40.864281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
4기평가년도5기평가년도한도설정금액건별발급금액성과공유금액납부예정금액최종수정수처리직원번호성과공유금액약정체결여부성과공유금액약정체결사유코드중단일자성과공유금액최초납부기한성과공유금액분납신청여부성과공유금액분납예정일자최초처리시각최초처리직원번호
4기평가년도1.0001.000-0.294-0.194-0.198-0.117-0.0680.4130.4820.4500.4080.2780.0000.4770.485
5기평가년도1.0001.000-0.294-0.194-0.198-0.117-0.0680.4130.4820.4500.4080.2780.0000.4770.485
한도설정금액-0.294-0.2941.0000.8720.8800.329-0.0270.4920.3200.5370.9840.7270.3120.0000.000
건별발급금액-0.194-0.1940.8721.0000.9980.309-0.0370.2470.1770.2730.5220.4920.4700.0000.000
성과공유금액납부예정금액-0.198-0.1980.8800.9981.0000.314-0.0360.3870.3200.4240.7890.6720.5470.0000.000
최종수정수-0.117-0.1170.3290.3090.3141.000-0.0150.0000.2360.0000.2080.2390.3300.0000.000
처리직원번호-0.068-0.068-0.027-0.037-0.036-0.0151.0000.0410.0000.0660.1050.1000.1170.0000.000
성과공유금액약정체결여부0.4130.4130.4920.2470.3870.0000.0411.0000.4500.7520.7210.5080.1020.4580.465
성과공유금액약정체결사유코드0.4820.4820.3200.1770.3200.2360.0000.4501.0000.5180.4800.3390.0000.4930.448
중단일자0.4500.4500.5370.2730.4240.0000.0660.7520.5181.0000.5480.5530.0660.3270.351
성과공유금액최초납부기한0.4080.4080.9840.5220.7890.2080.1050.7210.4800.5481.0000.9990.1510.3360.359
성과공유금액분납신청여부0.2780.2780.7270.4920.6720.2390.1000.5080.3390.5530.9991.0000.8270.1190.174
성과공유금액분납예정일자0.0000.0000.3120.4700.5470.3300.1170.1020.0000.0660.1510.8271.0000.0000.000
최초처리시각0.4770.4770.0000.0000.0000.0000.0000.4580.4930.3270.3360.1190.0001.0000.991
최초처리직원번호0.4850.4850.0000.0000.0000.0000.0000.4650.4480.3510.3590.1740.0000.9911.000

Missing values

2023-12-12T09:29:33.910351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:29:34.209498image/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.
2023-12-12T09:29:34.374482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

업무구분코드원장번호성과공유금액약정체결여부성과공유금액약정체결사유코드4기평가년도5기평가년도중단일자한도설정금액건별발급금액성과공유금액납부예정금액성과공유금액최초납부기한성과공유금액분납신청여부성과공유금액분납예정일자성과공유금액유예기한삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
0GTIF200700832Y1201020110001-01-01 00:00:00.00000030000000083500000167000000:00.0N0001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N1005:08.439470001-01-01 00:00:00.000000BATCH
1GIIA200801114Y1201120120001-01-01 00:00:00.000000200000000220000000400000000:00.0N0001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N905:37.434190001-01-01 00:00:00.000000BATCH
2GTHZ200801078Y1201120120001-01-01 00:00:00.0000000000001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N208:47.644350001-01-01 00:00:00.000000BATCH
3GTIE200800236N12011201200:00.00000001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N340:54.748130001-01-01 00:00:00.000000BATCH
4GTAQ201200149<NA><NA>0001-01-01 00:00:00.0000000000001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N236:10.736700001-01-01 00:00:00.000000BATCH
5GTQA201000562N<NA><NA>00:00.00000001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N132:10.4479232:10.44792
6GTQA201200218<NA><NA>00:00.00000001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N1142:36.947920001-01-01 00:00:00.000000BATCH
7GIIA200800626Y1201120120001-01-01 00:00:00.00000024000000080000000160000000:00.0Y0001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N516:09.240840001-01-01 00:00:00.000000BATCH
8GTQD201100691N201420150001-01-01 00:00:00.0000000000001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N620:41.535140001-01-01 00:00:00.000000BATCH
9GTHW201200215N2015201600:00.00000001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N657:45.146700001-01-01 00:00:00.000000BATCH
업무구분코드원장번호성과공유금액약정체결여부성과공유금액약정체결사유코드4기평가년도5기평가년도중단일자한도설정금액건별발급금액성과공유금액납부예정금액성과공유금액최초납부기한성과공유금액분납신청여부성과공유금액분납예정일자성과공유금액유예기한삭제여부최종수정수처리시각처리직원번호최초처리시각최초처리직원번호
490GICA200800567Y1201120120001-01-01 00:00:00.0000000000001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N118:36.1425518:36.14255
491GIPA200800377Y1201120120001-01-01 00:00:00.00000015000000050000000100000000:00.0N0001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N455:33.436500001-01-01 00:00:00.000000BATCH
492GTAP200701024Y1201020110001-01-01 00:00:00.0000000000001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N259:04.937670001-01-01 00:00:00.000000BATCH
493GTHJ200800775N201120120001-01-01 00:00:00.0000000000001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N332:03.241470001-01-01 00:00:00.000000BATCH
494GIIA200800846Y1201120120001-01-01 00:00:00.0000000000001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N206:41.025640001-01-01 00:00:00.000000BATCH
495GIIA200800027Y1201120120001-01-01 00:00:00.0000000000001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N451:01.325640001-01-01 00:00:00.000000BATCH
496GTNA200701089Y1201020110001-01-01 00:00:00.000000500000000370000000740000000:00.0N0001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N411:30.539730001-01-01 00:00:00.000000BATCH
497GIIA200801264Y1201120120001-01-01 00:00:00.0000000000001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N351:11.525640001-01-01 00:00:00.000000BATCH
498GTHU200800192Y201120120001-01-01 00:00:00.000000300000000128000000256000000:00.0N0001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N245:17.930820001-01-01 00:00:00.000000BATCH
499GTAZ200700725N12010201100:00.00000001-01-01 00:00:00.0000000001-01-01 00:00:00.0000000001-01-01 00:00:00.000000N258:42.338630001-01-01 00:00:00.000000BATCH